Guidance for analytical methods to cumulative effects ... - Tethys
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REVIEW Guidance for analytical methods to cumulative effects assessment for terrestrial species C. Lisa Mahon and Shawna Pelech Abstract: Landscapes in Canada are undergoing change due to resource and land use stressors and climate stressors. Under- standing the cumulative effects of these stressors is challenging because of the complexity of ecosystems, the variability of stres- sors, and species response to individual or multiple stressors. A key challenge within the field of cumulative effects assessment (CEA) is guidance that describes and evaluates analytical methods. In this review we discuss four broad categories of methods with current or potential use for project-based and effects-based CEA for species in terrestrial systems: (i) qualitative review, (ii) habitat supply models, (iii) empirical species–stressor models, and (iv) decision support models. We describe each method and provide examples, highlight advantages and limitations, identify how methods address key science-based CEA questions, and provide direction on when and why to use specific CEA methods. Empirical species–stressor models and decision support models are the only analytical approaches that provide answers to many science-based CEA questions including how multiple stressors combine to affect an individual species and the certainty of multiple stressor effects. We provide recommendations for using one or more methods as complementary building blocks to fill data gaps, improve understanding and communication, engage diverse partner groups, and increase the quality and credibility of the CEA. Our review supports a move toward regional scale, effects-based CEA where partner collaboration to design, implement, and analyze comprehensive assessments of multiple stres- sors will (i) expand our knowledge of terrestrial species response to stressors and (ii) inform best management practices for resource industries and conservation and management actions for land managers. Key words: analytical methods, Canada, cumulative effects assessment, models, stressors, terrestrial species. Résumé : Les paysages au Canada sont en train de changer en raison de facteurs de stress liés aux ressources et à l’utilisation des terres et de facteurs de stress climatiques. Il est difficile de comprendre les effets cumulatifs de ces facteurs de stress en raison de la complexité des écosystèmes, de la variabilité des facteurs de stress et de la réponse des espèces à des facteurs de stress individ- uels ou multiples. Un des principaux défis dans le domaine de l’évaluation des effets cumulatifs (EEC) est l’orientation qui décrit et évalue les méthodes analytiques. Dans cette synthèse, les auteurs examinent quatre grandes catégories de méthodes actuelle- ment ou potentiellement utilisées pour l’EEC des projets et des effets sur les espèces dans les systèmes terrestres : (i)l’examen qualitatif, (ii) les modèles d’approvisionnement de l’habitat, (iii) les modèles empiriques de facteurs de stress des espèces et (iv) les modèles d’aide à la décision. Ils décrivent chaque méthode et fournissent des exemples, soulignent les avantages et les lim- ites, identifient comment les méthodes répondent aux questions clés de l’EEC basée sur la science, et fournissent une orientation sur le moment et la raison d’utiliser des méthodes d’ECC spécifiques. Les modèles empiriques de facteurs de stress des espèces et les modèles d’aide à la décision sont les seules approches analytiques qui fournissent des réponses à de nombreuses questions d’EEC fondées sur la science, notamment la manière dont de multiples facteurs de stress se combinent pour affecter une espèce individuelle et la certitude des effets de multiples facteurs de stress. Ils fournissent des recommandations quant à l’utilisation d’une ou de plusieurs méthodes comme éléments de base complémentaires pour combler les lacunes des données, améliorer la compréhension et la communication, engager divers groupes de partenaires et accroître la qualité et la crédibilité de l’EEC. Leur synthèse soutient un changement vers une EEC à l’échelle régionale, où la collaboration des partenaires pour concevoir, mettre en œuvre et analyser des évaluations complètes de multiples facteurs de stress permettra (i)d’élargir nos connaissances sur la réponse des espèces terrestres aux facteurs de stress et (ii) de définir les pratiques optimales de gestion pour les industries prim- aires et les mesures de conservation et de gestion pour les gestionnaires des terres. [Traduit par la Rédaction] Mots-clés : méthodes analytiques, Canada, évaluation des effets cumulatifs, modèles, facteurs de stress, espèces terrestres. 1. Introduction Landscapes throughout much of Canada have been subject to an increasing amount and variety of human-induced disturban- ces over the past decades. In many cases, these changes have resulted from a combination of resource sectors (forestry, oil/gas industries, mining, ranching) and land use practices (agricul- ture, urban and rural development) and their associated stres- sors (e.g., harvest units, agriculture plots, well sites, pipelines, industrial sites, mine sites, roads, powerlines, rail lines). In addi- tion, climate change stressors or variables (e.g., temperature change, Received 23 April 2020. Accepted 6 November 2020. C.L. Mahon. Environment and Climate Change Canada, 91780 Alaska Highway, Whitehorse, YT Y1A 5X7, Canada; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada. S. Pelech. 143 Cypress Court, Saskatoon, SK S7K 5C3, Canada. Corresponding author: C. Lisa Mahon (email: [email protected]). Copyright remains with the author(s) or their institution(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. Environ. Rev. 29: 201–224 (2021) dx.doi.org/10.1139/er-2020-0037 Published at www.nrcresearchpress.com/er on 8 May 2021. 201 Environ. Rev. Downloaded from cdnsciencepub.com by BATTELLE PACIFIC NW NATL LAB on 12/07/21 For personal use only.
Transcript of Guidance for analytical methods to cumulative effects ... - Tethys
REVIEW
Guidance for analytical methods to cumulative effectsassessment for terrestrial speciesC Lisa Mahon and Shawna Pelech
Abstract Landscapes in Canada are undergoing change due to resource and land use stressors and climate stressors Under-standing the cumulative effects of these stressors is challenging because of the complexity of ecosystems the variability of stres-sors and species response to individual or multiple stressors A key challenge within the field of cumulative effects assessment(CEA) is guidance that describes and evaluates analytical methods In this review we discuss four broad categories of methodswith current or potential use for project-based and effects-based CEA for species in terrestrial systems (i) qualitative review(ii) habitat supply models (iii) empirical speciesndashstressor models and (iv) decision support models We describe each method andprovide examples highlight advantages and limitations identify how methods address key science-based CEA questions andprovide direction on when and why to use specific CEA methods Empirical speciesndashstressor models and decision support modelsare the only analytical approaches that provide answers to many science-based CEA questions including how multiple stressorscombine to affect an individual species and the certainty of multiple stressor effects We provide recommendations for usingone or more methods as complementary building blocks to fill data gaps improve understanding and communication engagediverse partner groups and increase the quality and credibility of the CEA Our review supports a move toward regional scaleeffects-based CEA where partner collaboration to design implement and analyze comprehensive assessments of multiple stres-sors will (i) expand our knowledge of terrestrial species response to stressors and (ii) inform best management practices forresource industries and conservation and management actions for land managers
Key words analytical methods Canada cumulative effects assessment models stressors terrestrial species
Reacutesumeacute Les paysages au Canada sont en train de changer en raison de facteurs de stress lieacutes aux ressources et agrave lrsquoutilisation desterres et de facteurs de stress climatiques Il est difficile de comprendre les effets cumulatifs de ces facteurs de stress en raison dela complexiteacute des eacutecosystegravemes de la variabiliteacute des facteurs de stress et de la reacuteponse des espegraveces agrave des facteurs de stress individ-uels ou multiples Un des principaux deacutefis dans le domaine de lrsquoeacutevaluation des effets cumulatifs (EEC) est lrsquoorientation qui deacutecritet eacutevalue les meacutethodes analytiques Dans cette synthegravese les auteurs examinent quatre grandes cateacutegories de meacutethodes actuelle-ment ou potentiellement utiliseacutees pour lrsquoEEC des projets et des effets sur les espegraveces dans les systegravemes terrestres (i) lrsquoexamenqualitatif (ii) les modegraveles drsquoapprovisionnement de lrsquohabitat (iii) les modegraveles empiriques de facteurs de stress des espegraveces et(iv) les modegraveles drsquoaide agrave la deacutecision Ils deacutecrivent chaque meacutethode et fournissent des exemples soulignent les avantages et les lim-ites identifient comment les meacutethodes reacutepondent aux questions cleacutes de lrsquoEEC baseacutee sur la science et fournissent une orientationsur le moment et la raison drsquoutiliser des meacutethodes drsquoECC speacutecifiques Les modegraveles empiriques de facteurs de stress des espegraveces etles modegraveles drsquoaide agrave la deacutecision sont les seules approches analytiques qui fournissent des reacuteponses agrave de nombreuses questionsdrsquoEEC fondeacutees sur la science notamment la maniegravere dont de multiples facteurs de stress se combinent pour affecter une espegraveceindividuelle et la certitude des effets de multiples facteurs de stress Ils fournissent des recommandations quant agrave lrsquoutilisationdrsquoune ou de plusieurs meacutethodes comme eacuteleacutements de base compleacutementaires pour combler les lacunes des donneacutees ameacuteliorer lacompreacutehension et la communication engager divers groupes de partenaires et accroicirctre la qualiteacute et la creacutedibiliteacute de lrsquoEEC Leursynthegravese soutient un changement vers une EEC agrave lrsquoeacutechelle reacutegionale ougrave la collaboration des partenaires pour concevoir mettreen œuvre et analyser des eacutevaluations complegravetes de multiples facteurs de stress permettra (i) drsquoeacutelargir nos connaissances sur lareacuteponse des espegraveces terrestres aux facteurs de stress et (ii) de deacutefinir les pratiques optimales de gestion pour les industries prim-aires et les mesures de conservation et de gestion pour les gestionnaires des terres [Traduit par la Reacutedaction]
Mots-cleacutes meacutethodes analytiques Canada eacutevaluation des effets cumulatifs modegraveles facteurs de stress espegraveces terrestres
1 IntroductionLandscapes throughout much of Canada have been subject to
an increasing amount and variety of human-induced disturban-ces over the past decades In many cases these changes haveresulted from a combination of resource sectors (forestry oilgas
industries mining ranching) and land use practices (agricul-ture urban and rural development) and their associated stres-sors (eg harvest units agriculture plots well sites pipelinesindustrial sites mine sites roads powerlines rail lines) In addi-tion climate change stressors or variables (eg temperature change
Received 23 April 2020 Accepted 6 November 2020
CL Mahon Environment and Climate Change Canada 91780 Alaska Highway Whitehorse YT Y1A 5X7 Canada Department of Biological SciencesUniversity of Alberta Edmonton AB T6G 2E9 CanadaS Pelech 143 Cypress Court Saskatoon SK S7K 5C3 Canada
Corresponding author C Lisa Mahon (email lisamahoncanadaca)Copyright remains with the author(s) or their institution(s) This work is licensed under a Creative Commons Attribution 40 International License (CC BY 40)which permits unrestricted use distribution and reproduction in anymedium provided the original author(s) and source are credited
Environ Rev 29 201ndash224 (2021) dxdoiorg101139er-2020-0037 Published at wwwnrcresearchpresscomer on 8 May 2021
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precipitation change permafrost melt) and climate-inducedchanges to natural disturbance regimes (eg wildfire floods insectoutbreaks disease outbreaks) resulting from human-caused cli-mate change are also influencing landscapes and habitats (Stralberget al 2018) Climate stressors may combine with existing resourceand land use stressors to create novel and complex additive or inter-active effects (Didham et al 2007 Darling and Cocircteacute 2008) Theseindividual and combined stressors alter the quantity and quality ofnatural habitats Understanding the cumulative effects of stressorsis challenging because of the complexity of ecosystems the vari-ability of stressors and species response to individual or multiplestressors (Hodgson et al 2019 Mahon et al 2019)Evaluating the combined or cumulative effects of these human-
induced disturbances (hereafter stressors) on terrestrial species isnow central to many conservation and management (Gunn et al2014) research and monitoring (Burton et al 2014 Mahon et al2019) and land use planning (Johnson et al 2011 Chetkiewicz andLintner 2014 BC Ministry of Forests Lands and Natural ResourcesOperations 2016 Yukon Land Use Planning Council 2018) efforts inCanada Here we define cumulative effects (CE) as the combinedeffects of multiple stressors on species or ecosystems over timeand (or) space and stressor as all human-induced activities anddrivers We identify two key types of cumulative effects assess-ments (hereafter CEAs) within Canadian jurisdictions The firstare project-based CEAs (Dubeacute and Munkittrick 2001 Noble 2010)initiated as part of individual project environmental assessments(hereafter EA) These can include assessments within an Environ-mental Impact Statement to meet requirements of the CanadianEnvironmental Assessment Act 2012 or within an Impact Statement tomeet requirements of the Impact Assessment Act 2019 The second areeffects-based or regional CEAs (Dubeacute and Munkittrick 2001 Noble2010) initiated as (i) one component of Regional Assessments(Impact Assessment Act 2019) which are studies and analyses conductedby an appointed committee or by the Impact Assessment Agencyof Canada (IAAC) in areas of existing or proposed projects ldquotoinform planning and management of CEAs and inform projectimpact assessmentsrdquo (IAAC 2019 httpswwwcanadacaenimpact-assessment-agencyhtml) or (ii) regional projects or studies conductedby partners or groups within a region of interest (eg land useplanning region) Both types of CEAs occur within Canadianjurisdictions but project-based and effects-based CEAs differ intheir goals and objectives We note differences between thescience-based goal of most effects-based CEAs (to characterizeanalyze and quantify individual and multiple stressor effectson species and ecosystems within a region) and the more limitedgoal of project-based CEAs (to undergo project review and approvalwithin a defined regulatory process Duinker and Greig 2006Noble 2010 Gunn and Noble 2011 Westwood et al 2019) Largerregional scale effects-based CEAs are needed to quantify the CE ofmultiple stressors on species and ecosystems where diverse sectors(eg in terrestrial systems sectors like forestry agriculture energymining) and processes (climate change) operate simultaneously(spatially temporally) within a region Limitations of project-basedCEAs to assess CE at larger regional scales have been widelyreviewed (Duinker and Greig 2006 Gunn and Noble 2011 Gillinghamet al 2016 Jones 2016) and include (i) insufficient spatial scale andscope to assess a range of stressors (ii) limited resources and dataand (iii) uncoordinated inconsistent and fragmented individualassessmentsThe long history and regulatory requirements of project-based
CEA have generated a large body of examples federal and juris-dictional CEA frameworks technical guidance documents andscholarly discussion Notably lacking however is specific scien-tific guidance that describes and evaluates analytical methods(eg qualitative and quantitative analysis methods) that can beused for both project-based and effects-based CEAs where thefocus is on terrestrial species and systems Here qualitative refersto the assignment of a value (eg low or high) or ranking for a
variable of interest and quantitative refers to the measurementof a quantity for a variable of interest Regulatory guidance docu-ments and published reviews suggest CEA methods that rangefrom qualitative approaches like questionnaires interviewschecklists matrices and diagrams to quantitative approacheslike habitat suitability models species stressor models and sim-ulation models (CEAA 2014 2018 Cocklin et al 1992 Smit andSpaling 1995 Schultz 2010 Canter and Atkinson 2011 Duinkeret al 2013) but offer little guidance regarding when or why touse specific methods and which CEA questions can be addressedWhile this generality and flexibility is understandable given CEAguidance must cover a range of projects regions biophysical com-munities and valued components (environmental componentsconsidered most indicative critical or important to ecosystemservices or stakeholder values hereafter VC) it can also givethe false impression these methods are equally effective inanswering many key science-based CEA questions For exam-ple one of the most frequently cited rationales for undertakingCEA in both project-based and effects-based CEAs is the poten-tial for multiple stressors to create unexpected additive orinteractive effects (Didham et al 2007 Crain et al 2008 Darlingand Cocircteacute 2008) However as many CEA methods are based onexisting data or expert opinion very few are capable of testingfor novel or complex effectsHere our objective is to review and provide guidance on quali-
tative and quantitative CEAmethods for species in terrestrial sys-tems relevant to both project-based CEAs and effects-based CEAsWe focus on terrestrial systems and species because althoughsome methods are applicable to both terrestrial and aquatic sys-tems some quantitative tools necessary to facilitate CEA meth-ods (eg marine spatial planning tools multi-species models)have been specifically developed for use in marine or aquatic sys-tems In addition although recent reviews of CEA methods havebeen conducted in aquatic systems (see Hodgson and Halpern2019) no similar reviews have been conducted for terrestrial sys-tems and species Specifically we discuss four broad categories ofmethods with current or potential use for project-based andeffects-based CEAs (i) qualitative review (ii) habitat supply mod-els (iii) empirical speciesndashstressor models and (iv) decision sup-port models We describe each method and provide exampleshighlight advantages and limitations identify how methods addresskey science-based CEA questions and provide direction on whenand why to use specific CEA methods The larger scale need forpolicy shifts and associated mechanisms to promote the imple-mentation of methods reviewed here (ie coordinated collabo-rative regional CEAs) are beyond the scope of this documentand have been well reviewed elsewhere (eg Gillingham et al2016 Jones 2016 Hodgson et al 2019 Westwood et al 2019)Our intended audience includes current CEA practitioners
however we direct this document to biologists planners andpolicy makers new to CEA or considering CEA outside of regula-tory project-based CEA We expect many current practitionersespecially biologists conducting assessments will be familiarwith the methods we outline here Thus while we hope our dis-cussion helps consolidate and synthesize information for thoseindividuals we particularly hope it aids discussions with managersand policy makers to ensure terrestrial CEA frameworks allowfor rigorous design and analysis We structure our discussionreviewas follows (i) goals questions and objectives of CEA (ii) review ofCEAmethods and (iii) conclusions and key recommendations
2 Goals objectives and questions related tocumulative effects assessment (CEA)We acknowledge that goals and objectives for project-based
and effects-based CEAs differ because regulatory CEAs specific toCanadian jurisdictions differ from nonregulatory CEAs (Table 1)Project-based CEAs within provincial jurisdictions initiated before
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28 August 2019 follow technical guidance associated with theCanadian Environmental Assessment Act 2012 (CEAA 2014 2018)while those initiated after 28 August 2019 follow technical guid-ance associated with the Impact Assessment Act 2019 (IAAC 2019)Critical to the CEAA technical guidance (CEAA 2014 2018) andoperational policy statement (CEAA 2015) is identifying meas-ures that would mitigate adverse CE (step 3) and determiningthe significance of any adverse CE taking into account theimplementation of mitigation measures (step 4) Similarly newIAAC technical guidance (see httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancesection-22-factors-considered-descriptionshtml) requires the consideration of anyCE (section 22 (a) (ii)) and any measures that would mitigate anyadverse effects of the designated project (section 22 (a) (b)) Project-based CEAs within northern jurisdictions (Yukon TerritoryNorthwest Territories Nunavut) follow specific technical guidance(Table 1) The focus of most project-based CEAs in Canada is toidentify and mitigate the individual stressors associated with theproposed project This process involves determining whetherproject activities in the context of other past present or futureactivities will cause significant adverse CE after the implementationof proposedmitigationmeasuresEffects-based CEAs conducted as part of Regional Assessments
under the IAAC are studies conducted in areas of existing proj-ects or anticipated projects within provincial jurisdictions toinform planning and management of CE and inform projectimpact assessments The Minister of Environment and ClimateChange appoints a committee or asks the IAAC to conduct a Re-gional Assessment establishes the terms of reference for eachRegional Assessment and reviews the final Regional Assessmentreport Regional Assessments may differ in their goals activitiesand approaches that can include filling data gaps and analyzing
trends establishing thresholds and standard mitigation or iden-tifying regional development objectives and scenarios (httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidanceregional-assessment-impact-assessment-acthtml) We notethat Regional Assessments under the IAAC are a new process(two Regional Assessments have been initiated and only oneRegional Assessment has been completed as of November 2020)Specific guidance and direction would ensure that future RegionalAssessments meet objectives of planning assessing and managingCE Regional Assessments under the IAAC have the potential to bea valuable and useful process for assessing CE in a region usinginnovative methods approaches and tools We promote the useof rigorous CEA analytical methods within Regional Assessments to(i) quantify current and future impacts of human activities andclimate change (ii) manage where when and how human activityoccurs and (iii) produce alternate scenarios to balance humanactivity and species conservationEffects-based CEAs outside of regulatory processes can be con-
ducted in any jurisdiction in Canada by interested partnersgroups or agencies (eg academic institutions provincial or ter-ritorial government agencies environmental nongovernmentalagencies Indigenous communities or groups research groupsland use planning commissions) The focus of most nonregula-tory effects-based CEAs in Canada is often on assessing and ana-lyzing stressor-effect relationships and simulating land andresource development scenarios within a specific region of inter-est This typically involves quantifying regional effects of stres-sors often associated with multiple sectors identifying types ofstressor combinations measuring uncertainty and (or) simulat-ing the impacts of stressors on habitats and terrestrial speciesinto the future using various landscape climate resource ormanagement scenarios
Table 1 Types of cumulative effects assessment (CEA) within Canada
Type of CEA Jurisdiction Agency Guidance CEAmethodsapproach
Project-based CEA1 CEA within EAIA Provinces CEAA Examine cumulative effects (CE) of a project on VCs usingfive steps (1) scoping
IAAC Examine CE (changes to environment health social and economic conditions)usingfive steps preceding (interim guidance httpswwwcanadacaenimpact-assessment-agency CEAA 2014 2018)
YT YESAB Examine CE onVCs by considering project effects (magnitude duration timinglikelihood reversibility and spatial extent) and other past current or futureproject effects (httpswwwyesabca YESAB 2019)
NT MVEIRB Examine CE using four steps (1) identifying parts of the environment affectedby project (2) determining parts of the environment affected by other pastpresent or future projects (3) predicting effects of proposed project andother projects (4) identifying ways tomanage combined impacts (httpsreviewboardca MVEIRB 2004)
NU NIRB Identify and document cumulative impacts from the project and any otherpast current or future project and determine whether project is of regionalinterest (httpswwwnirbca NIRB 2020)
Effects-based CEA1 Regional Assessment Provinces IAAC Examine CE using methods identified by the appointed committee or the
IAAC and documented in the terms of reference2 Regional Projects Any location or
Examine CEwithin region of interest using questions andmethods defined bypartners
Note Jurisdiction definitions mdash YT Yukon Territory NT Northwest Territories NU Nunavut Agency definitions mdash CEAA Canadian Environmental AssessmentAgency IACC Impact Assessment Agency of Canada administers projects under Canadian Environmental Assessment Act 2012 and Impact Assessment Act 2019 YESABYukon Environmental and Socio-economic Assessment Board administers projects under Yukon Environmental and Socio-economic Assessment Act 2003 MVEIRBMackenzie Valley Environmental Impact Review Board administers projects underMackenzie Valley Resource Management Act 1998 NIRB Nunavut Impact Review Boardadministers projects under Nunavut Planning and Project Assessment Act 2013 partners can be academic institutions provincial or territorial government agenciesenvironmental non-governmental agencies Indigenous communities or groups research groups land use planning commissions Acronyms EA environmentalassessment IA impact assessment VC valued components
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There are multiple CEA definitions frameworks (see Duinkeret al 2013) processes and questions (as described previously andin Table 1) In this review we define key science-based CEA objec-tives relevant to both project-based and effects-based CEAs as thefollowing (i) describe or characterize overall CE (ii) determinethe rank or magnitude and direction (eg positive or negative) ofindividual stressor effects (iii) describe the shape of the relation-ship between stressors and species (eg linear or nonlinear)(iv) determine how stressors combine to affect species (eg addi-tive interactivendashsynergistic interactivendashantagonistic compensa-tory or masking) (v) relate CE to baseline or pre-disturbanceconditions defined thresholds or management triggers and(vi) describe or assign certainty or probability of occurrence ofpredicted effects Within our review we assess the capacity ofCEA methods for species in terrestrial systems to address thesescience-based CEA objectives
3 Description and evaluation of CEAmethodsHere we provide an overview of current methods for project-
based and effects-based CEAs that have or could be used for terres-trial species We conducted a three-stage review to (i) identify CEAmethods used in project-based CEAs and effects-based CEAs and(ii) document relevant examples First we reviewed all active assess-ments (as of 15 June 2020) listed on the Canadian Impact AssessmentRegistry (httpsiaacgcca) including Environmental Assess-ments under Canadian Environmental Assessment Act 2012 (n = 50)Environmental Assessments by Review Panel under CanadianEnvironmental Assessment Act 2012 (n = 8) Impact Assessmentsunder Impact Assessment Act 2019 (n = 2) and Regional Assessmentsunder Impact Assessment Act 2019 (n = 2) and recorded the types ofmethods used within the CEA (eg Environmental ImpactStatement Impact Statement Regional Assessment) in Table 2Second we searched for published reviews of CEA methods
within the scientific literature and the CEA guidance literature(Table 2) We collapsed similar approaches identified in stages 1and 2 into four broad categories of CEA methods qualitativereview habitat supply models empirical speciesndashstressor modelsand decision support models (focusing on Bayesian belief network(BBN) models) We distinguished data products like indicators (iea measurable variable chosen to represent the state of a compo-nent) and indices (ie composite numerical value for some envi-ronmental attribute usually comprised of two or more variables)that can be used within a CEA process from analytical CEAmethods We also distinguished data tools like geographic in-formation systems (GIS) used to produce spatial data productsincluded within most modern project-based or effects-basedCEAs from analytical CEAmethodsThird we conducted targeted searches using Web of Science
across the four CEA methods to find published examples for spe-cies in terrestrial systems We include some CEA publishedpapers as examples in the description of each CEAmethod in sub-sequent sections and list all papers reviewed by CEA methodincluding study area location and target terrestrial species orgroup (Table 3) CEA methods fall on a gradient of increasingcomplexity data requirements and technical expertise In manycases a combination of CEA methods will be required to effec-tively answer science-based CEA questions Our intent is not anexhaustive review of all CEA methods or an intensive review ofone category of methods Instead we describe each CEA methodand provide relevant examples We also identify advantages andlimitations explain how methods address science-based CEAquestions (Table 4) and provide direction on when and why touse specific CEAmethods
31 Qualitative review
311 Description of methodThis broad category of methods includes a variety of approaches
to collect (questionnaires interviews workshops literature reviews)
and summarize (checklists matrices qualitative or conceptualmodelsdiagrams) qualitative data or information (eg expertopinion published or unpublished information) as an exclusiveor primarymeans to assess CE (Cocklin et al 1992 Smit and Spaling1995 Duinker et al 2013 CEAA 2014 2018 see Tables 2 and 3)rather than direct measurements of species response to habitatchange or stressors This approach would typically include assess-ments with high constraints on data availability (for species habi-tats and stressors) time and (or) cost For example informationon stressors may be based on qualitative descriptions or approxi-mations of development (eg hard copies of maps written orverbal descriptions of proposed developments or stressors) ratherthan spatial and attribute data on stressors within a GIS In somecases qualitative datainformation is ranked to provide a categori-cal assessment of effects (eg using literature or expert review torankweight potential effects of stressors on species) Formal con-sensus techniques and qualitative modelling can also be used (egDelphi method see Linstone and Turoff 2002 fuzzy cognitivemapping see Gray et al 2015) although these remain infrequentfor most assessments of terrestrial species Although also a formof qualitative review we consider independent traditional knowl-edge (TK) studies or combined TK and scientific knowledge (SK) stud-ies (eg co-production of knowledge) to be separate from thiscategory due to distinct methods used to select experts with dif-ferent types of knowledge (eg local harvesters and huntersElders government employees scientists) and conduct the elici-tation procedure (Huntington 1998 Usher 2000 Polfus et al 2014Beacutelisle et al 2018)Qualitative review is common among project-based CEAs for
terrestrial species (see Table 2 stage 1) where proponents typi-cally conduct literature reviews to collect information and usechecklists and matrices to rank potential effects on VC (eg lowto high) Qualitative review is less common among effects-basedCEAs for terrestrial species (outside of TK studies) with the excep-tion of (i) potential stressor effects that are difficult to map orquantify (eg pesticide use pollution species interactions) and(ii) complex systems where multiple stressor effects are difficultto visualize or comprehend In the latter case conceptual modelsdiagrams can be combined with other quantitative CEA approaches(eg habitat supply empirical speciesndashstressor and decision supportmodels) where the conceptual model is shared refined and usedto guide monitoring and research questions and hypothesesamong partners (Marcot et al 2006 Lindenmayer and Likens 2010Nelitz et al 2015)
312 Examples of applicationsWe highlight two examples of qualitative review methods and
list additional examples from the published literature in Table 3The first example from a recent project-based CEA from theIAAC Registry (Environmental Impact Statement for the MarathonPGM-Cu Project 2012) uses a matrix table (Table 5) to identify theVCs potential effects mitigation measures and residual effectsof the project For each VC gray wolf (Canis lupus) and Americanblack bear (Ursus americanus) a rank or level is assigned to eachparameter used to characterize residual effects (magnitude spa-tial extent frequency duration reversibility ecological or soci-etal value) These levels are then combined to create a measureof overall significance a required first step in conducting theCEA (CE are defined as the environmental effects resulting fromthe project in combination with other projects or activities) Thesecond example from an effects-based CEA uses a conceptualmodeldiagram to illustrate the Greater Sage-Grouse (Centrocercusurophasianus) annual life cycle including influence of oil and gasdevelopment on site fidelity dispersal and demography (Fig 1)The life cycle informs the population simulation model used totrack population dynamics and habitat use of simulated Sage-Grousethrough space and time as they respond to changing landscape
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conditions (climate-induced vegetation change) and exposure tooil and gas stressors (Heinrichs et al 2019)
313 Advantages and limitations CEA questions and whenwhyto use
3131 AdvantagesThe advantage of qualitative review is its utility when quantita-
tive data on habitat and stressors and time and (or) financialresources are limited The approach also has the ability to (i) offera collaborative flexible and accessible approach when localissues and values are important (eg TK or combined TK and SKstudies) (ii) identify a wide range of stressors and mechanisms
(eg indirect effects species interactions stressors that cannotbe effectively mapped) and (iii) serve as a conceptual frameworkwithin a CEA using one or more quantitative methods to assessand (or) simulate multiple stressor effects (eg empirical speciesndashstressor models Bayesian belief network (BBN) models simulationmodels)
3132 LimitationsThe limitation of qualitative review is uncertainty surrounding
the objectivity and reliability of assessments particularly whenexpert opinion is applied to novel locations or stressors Specifi-cally these limitations include the inability to quantify any stres-sor related impacts including effects on habitat area (eg amount
Table 2 Cumulative effects assessment (CEA) methods identified in the stage one review (Canadian Impact Assessment Registry) and the stagetwo review (published reviews of CEAmethods and publicly available CEA guidance documents)
Stage CEAmethods
Stage 1 Canadian Impact Assessment Registry Total 2262 (35) with Environmental Impact Statement ImpactStatement Regional Assessment Report
Environmental Assessment under Canadian Environmental Assessment Act2012 (n = 50)
1650 (32) with Environmental Impact Statement1516 used QR416 used QR and HSM
Environmental Assessment by Review Panel under CanadianEnvironmental Assessment Act 2012 (n = 8)
58 (625) with Environmental Impact Statement55 used QR35 used QR and HSM15 used QR HSM and SM
Impact Assessment under Impact Assessment Act 2019 (n = 2) 02 (0) with Impact StatementRegional Assessment under Impact Assessment Act 2019 (n = 2) 12 (50) with Regional Assessment Report
1 used QR HSM and DSM
Stage 2 Reviews of CEAmethodsCocklin et al 1992 Environmental checklists matrix approaches inputndashoutputmethods
approaches including network analysisCanter and Atkinson 2011 Indicators and indices habitat suitability modelsDuinker et al 2013 GIS scenarios thresholds indicators and indices network analysis
matrices habitat suitability modelshabitat selection modelshabitat supply models stressor-based and effects-based approachesmultivariate statistical analysis air pollutionmodels landscapemodels animal population simulation models water-basedsimulationmodels public engagement community-basedmonitoring population viability analysis costndashbenefit analysisvisual amenity analysis traffic-light decision support systemsustainability appraisal
Hodgson and Halpern 2019 Experiment meta-analysis single species models mappingqualitative models multi-species models (aquaticmarine)
Marcot et al 2001 Marcot et al 2006 McCann et al 2006 Uusitalo 2007 Bayesian networkmodelsBayesian belief network (BBN) models
Stage 2 CEA guidance documentsTechnical Guidance for Assessing CEA under Canadian EnvironmentalAssessment Act 2012 Draft (CEAA 2014) Technical Guidance forAssessing CEA under Canadian Environmental Assessment Act 2012InterimGuidance (CEAA 2018)
Questionnaires and interviews checklists and matrices network andsystems analysisdiagrams indicators and indices conceptual andnumerical models trend analysis spatial analysis
Section 22-Factors to be considered (IAAC 2019 httpswwwcanadacaenimpact-assessment-agency)
New IAAC guidance in preparation interim guidance is CEAA (2018)
Consideration of cumulative effects in YESAB Assessments (YESAB 20052019)
No CEAmethods
Environmental Assessment Guidelines (MVEIRB 2004) No CEAmethodsProponents Guide NIRB Technical Guide Series (NIRB 2020) No CEAmethods
Note Agency definitions CEAA Canadian Environmental Assessment Agency IACC Impact Assessment Agency of Canada administers projects under CanadianEnvironmental Assessment Act 2012 and Impact Assessment Act 2019 YESAB Yukon Environmental and Socio-economic Assessment Board administers projects underYukon Environmental and Socio-economic Assessment Act 2003 MVEIRB Mackenzie Valley Environmental Impact Review Board administers projects underMackenzie ValleyResource Management Act 1998 Nunavut Impact Review Board (NIRB) administers projects under Nunavut Planning and Project Assessment Act 2013 CEA methoddefinitions QR qualitative review HSM habitat supply models SM simulation models DSM decision support models
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of suitable habitat lost) and direct (eg habitat loss mortality) orindirect stressor effects on species (eg altered habitat selectionreduced demographic rates like reproduction or survival)
3133 Science-based CEA questionsSee Table 4 to identify how qualitative review methods address
key science-based CEA questions
3134 Whenwhy to usemdash project-based CEAsldquoWhen to userdquo when quantitative data on species and spatial
data on habitats or stressors limit the development of speciesndashhabitatmodels (to assess change in habitat) and (or) speciesndashhabitatndashstressor models (to assess species response to habitat and stressors)Qualitative data and qualitative reviewmethods can be used to iden-tify rank and categorize stressor effects ldquoWhy to userdquo allows for acomprehensive listing and ranking of potential stressor effects onVCs in relation to (i) mitigation measures for the proposed projectand (ii) other past current or future projects
3135 Whenwhy to usemdash effects-based CEAsldquoWhen to userdquo when systems are complex conceptual diagrams
can be combined with other quantitative methods and used to(i) identify multiple stressors and (or) pathways of effects on dif-ferent life stages of target species or VCs (see Fisher and Burton2018 Pirotta et al 2018 and Heinrichs et al 2019 for examples)and (ii) outline the analysis process or framework (see Munns2006 and Katzner et al 2020 for examples) ldquoWhy to userdquo allowsfor a visual representation of the system (stressors ecologicaldrivers individual and population responses) and the analysisprocess
32 Habitat supply models
321 Description of methodIn this category we include various forms of speciesndashhabitat
modelling currently the most common approach to terrestrialspecies assessment in most project-based CEAs as well as in con-servation planning and landscape management (eg Morrisonet al 2006) Approaches vary in complexity from simple literature-based habitat rankings to complex statistical models of empirical
speciesndashhabitat relationships (see Table 3) A defining characteristicof this method for CEA is that habitat is used as a surrogate for theidentified terrestrial species (or as a VC itself) For habitat supplymodels quantitative changes in the amount and (or) quality of aspeciesrsquo habitat (typically categorized and measured within a GIS)are used as the primary measure of CE In contrast for empiricalspeciesndashstressormodels the species direct response to one ormorestressors is used as the primary measure of CE We provide morescientific background to this approach subsequently however ingeneral this approachwith respect to CEA typically involves
1 Developing speciesndashhabitat associations or models Thisrequires ranking the suitabilityeffectivenessfunctionality ofmeasurable habitat categories for target species using expertopinion literature review and (or) empirical speciesndashhabitatcorrelations
2 Quantifying changes in the amount of this habitat as a resultof the proposed project or projects and existingfuture stressors
3 Inferring effects on species as a result of these habitat changes
Speciesndashhabitat models can be developed using a variety ofexpert opinion and statistical modelling approaches have a longhistory in wildlife management and landscape planning and arewell-documented in the literature (eg Morrison et al 2006Millspaugh and Thompson 2009 Guisan et al 2013) Standardsfor developing literature and expert opinion based habitat suit-ability indices (HSI) date back to the 1980s (US Fish and WildlifeService (USFWS) 1980 1981) and many jurisdictions have simi-larly developed protocols for wildlife habitat rankings in Canada(BC Resource Inventory Committee 1999 Clarke 2012) Thesehabitat suitability models typically use experts and (or) compilespecies information from existing literature to rank habitats fora species (eg on a 0ndash1 scale or similar categorical ranking sys-tem) Additional variables that may modify habitat quality (egproximity to roads) are also often considered to differentiatebetween habitat capability (the potential of the habitat to sup-port the species under optimal conditions) and habitat suitabilityor effectiveness (the actual ability of the habitat to support the spe-cies in a specific study area)
Table 3 Cumulative effects assessment (CEA) methods identified in the stage three review (targeted reviews of CEA methods) qualitativereview habitat supply models empirical speciesndashstressor models and decision support models (Bayesian belief network (BBN) models)
Stage 3 Targeted reviews of CEAmethods References
Qualitative review Chilima et al 2013 (Ontario CAN watersheds) Cooper 2011 (United Kingdom multiple species)Swor and Canter 2011 (Pennsylvania USA water fish mussels riparianfloodplain resources)
Habitat supply models Canter and Atkinson 2011 (USA multiple species) Houle et al 2010 (Quebec CAN gray wolf)Johnson et al 2005 (Northwest Territories CAN caribou gray wolf grizzly bear wolverine) Maceet al 1999 (Montana USA grizzly bear) Roumldder et al 2016 (Cologne Germany Sand Lizard)
Empirical species-stressor models Sawyer et al 2009 (Wyoming USA mule deer) Houle et al 2010 (Quebec CAN gray wolf) Salice et al2011 (South Carolina USA Eastern Narrow-mouthed Toad) Salice 2012 (southeast USA EasternNarrow-mouthed Toad) Wilson et al 2013 (northwest Alaska USA caribou) Goodale andMilman2019 (offshore regions waterfowl waterbirds landbirds) Hodgson et al 2017 (various locationsinsects birds fish ungulates) Jones et al 2017 (Muskoka RiverWatershed CAN benthic invertebrates)Fisher and Burton 2018 (northeast Alberta CAN gray wolf white-tailed deermoose American blackbear coyote Canada lynx fisher red fox snowshoe hare American red squirrel) Claireau et al 2019(western France 10 bat species) Daniel and Koper 2019 (southern Alberta CAN 5 grassland landbirdspecies) Heinrichs et al 2019 (Wyoming USA Greater Sage-Grouse) Mahon et al 2019 (northeastAlberta CAN 27 boreal landbird species) Katzner et al 2020 (California USA roadrunner hawk)Leston et al 2020 (northeast Alberta CAN 20 boreal landbird species)
McNay et al 2006 (north-central British Columbia CAN caribou) Steventon et al 2006 (north coastof British Columbia CAN Marbled Murrelet) Howes et al 2010 (eastern Australia woodlandlandbirds) MacCracken et al 2013 (Bering and Chukchi Seas Pacific walrus) Ban et al 2014 (GreatBarrier Reef Australia reef coral) Fortin et al 2016 (Alaska USA brown bears) Mantyka-Pringleet al 2016 (South East Queensland Australia macroinvertebrates fish) Mantyka-Pringle et al 2017(Slave River Delta Northwest Territories CAN fish waterfowl mammals)
206 Environ Rev Vol 29 2021
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r pe
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Tab
le4
Summaryco
mparison
ofan
alytical
method
sforcu
mulative
effectsassessmen
t(CEA)for
terrestrialspecies
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Cumulative
effectsof
stressors
bas
edon
Exp
ertknow
ledge
literature
review
oflikelyeffects
Estim
ated
chan
gein
amou
ntof
suitab
lehab
itat
forsp
eciesas
aresu
ltof
stressorssu
itab
lehab
itat
isch
arac
terize
dusingex
pert
opinionliterature
and(or)
empirical
data
Directmea
suremen
tsof
speciesrsquo
resp
onse
tograd
ientof
stressors
andco
mbinationsof
stressors
Mod
eled
respon
seof
speciesab
unda
nce
orhab
itat
tocombined
hab
itatstressor
andother
inpu
ts(in
putsmay
bequ
alitativeor
empiricald
ata)
Most
commonly
asso
ciated
with
Project-based
CEA
Project-based
CEAscien
tific
research
Effects-based
CEAscien
tific
research
Effects-based
CEAscien
tificresearch
Key
strength
s
Limited
data
time
andco
strequirem
ents
Welle
stab
lish
edap
proach
Addresses
allo
rmostCEAgo
als
question
susingdirectmea
sures
ofsp
ecies
stressor
relation
ship
Can
incorporatemultipleda
tatype
san
dsources(qua
litativean
dqu
antitative
data)
Can
includeawiderange
ofstressors
Allow
squan
tification
ofeffects
(egloss
ofsu
itab
lehab
itat)w
hen
directsp
eciesndashstressor
resp
onse
orsp
eciesdataarelimited
Other
strengthslimitationsba
sedon
theex
tentto
whichqu
alitativeversus
quan
titative
dataareused
Key
limitations
Fa
ilsto
answ
erCEAquestion
sor
doe
sso
withuncertainreliab
ility
Inco
rporates
effectson
lydueto
hab
itat
loss
Highdata
timean
dco
strequirem
ents
Infreq
uen
tuse
interrestrialsystems
may
limitap
plication
Accuracy
andreliab
ilityhighly
dep
enden
ton
qualityan
dprecision
ofsp
ecies
hab
itat
associations
Restrictedto
speciesthat
can
beread
ilysampled
Fa
ilsto
address
man
yCEA
objectives
(interactionssh
ape
ofsp
eciesndashstressor
resp
onse)
Quan
titative
datarequirem
ents
Spec
ies
Nonemdash
Not
quan
titative
Lo
wmdash
Ifbased
onqualitative
species
hab
itat
associations
Veryhighmdash
Req
uires
species
metrics
across
fullgrad
ientof
stressors(andco
mbinationsif
testingforinteractions)
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareusedin
themod
el
Highmdash
Ifbased
onem
pirical
data
species
hab
itat
associations
(see
text)
Hab
itat
mdashsp
atiald
ata
Nonemdash
Not
quan
titative
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Stressormdash
spatiald
ata
Nonemdash
Not
quan
titative
Highto
very
highmdash
Tobe
effectiverequires
accu
rate
accessible
spatiald
ataon
ldquoindividualrdquostressorsoftenbroad
sector
catego
ries
(mining
oilg
as)
areused
Veryhighmdash
Tobeeffective
requires
accu
ratea
ccessible
spatiald
ataon
ldquoindividualrdquo
stressors
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Mahon and Pelech 207
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Tab
le4(continu
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
answ
erkey
CEAquestionsan
dap
plica
tionofCEA
What
isth
erelative
mag
nitudean
ddirection
ofindividual
stressor
effects
Xmdash
Only
based
onqualitativedata
Based
onrelative
contribution
ofstressor
toreducingam
ountof
estimated
suitab
lehab
itat
(req
uires
ldquobackfillingrdquo
individual
stressorsin
GISsee
text)
Pa
rameter
estimates
inmod
elrepresentmag
nitudedirection
and
certainty
ofstressor
effects
Stressor
effectstypically
are
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Relativeco
ntribution
ofstressors
toloss
ofsu
itab
lehab
itat
canbe
analysed
(req
uires
ldquoback-fillingrdquo
individual
stressorsin
GISsee
text)
What
isth
esh
apeofth
esp
eciesndashstressorresp
onse
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edNon
linea
rrelation
ships
(polyn
omial)canbetested
via
mod
elselectionan
dread
ily
grap
hed
Sh
apeof
theresp
onse
canbe
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Dostressors
combin
ead
ditivelyorin
tera
ctively
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edInteractionterm
scanbetested
forinclusion
viamod
elselection
procedures
Interactioneffectscanbeestimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rceof
inputmay
bequalitativeinform
ationor
empirical
mod
els)
Coe
fficien
tsgraphscanbeusedto
catego
rize
synergisticve
rsus
antago
nisticeffects
What
isth
eove
rallim
pac
tofcu
mulative
effectson
populations
Xmdash
Only
based
onqualitative
data
Based
onloss
ofsu
itab
lehab
itat
Sp
eciesndashhab
itatndashstressor
mod
els
canbeusedto
predictan
dco
mpare
speciesab
undan
cemea
sures
amon
gldquon
o-disturban
cereferen
cerdquo
curren
tan
dfuture
landscap
eco
nditionsbased
onGISla
ndscap
esimulation
Po
pulation
resp
onseoutputcanbe
compared
amon
gldquon
o-disturban
ce
referencerdquocu
rren
tan
dfuture
scen
arioswithmod
elinput(hab
itat
stressor
amou
nts)b
ased
onGIS
landscap
esimulation
Sp
eciesndashhab
itat
mod
elprediction
sforhab
itat
amou
nts
orsp
ecies
abunda
nce
canbe
compa
redam
ong
ldquono-disturban
cereferen
cerdquocu
rren
tan
dfuture
landscapecondition
sba
sedon
GISla
ndscapesimulation
What
isth
eove
rallim
pac
tonth
eco
mmunity
Xmdash
Only
based
onqualitative
data
Req
uires
calculatingspeciesndashhab
itat
mod
elsforallspeciescalculating
predicted
abunda
ncesin
reference
curren
tor
future
landscapesand
comparingcommunitymetrics
amon
gthesepredictedcommun
ities
Withsu
fficien
tdata
community
metrics
canbeco
mpared
amon
glowh
ighstressor
catego
ries
Req
uires
calculatingpop
ulation
resp
onsesforsp
eciesin
reference
curren
tor
future
landscap
esa
nd
comparingco
mmunitymetrics
amon
gthesepredictedco
mmunities
Com
munitymetrics
cansimilarly
becompa
redusingspeciesabun
dance
inreference
curren
tor
future
landscap
esa
spredictedfrom
empiricalspeciesndashhab
itatndashstressor
mod
els
How
certainar
eresu
lts
How
cance
rtainty
be
assessed
Verylow
mdashSu
bjectivenature
mak
esitdifficu
ltto
quan
tify
certainty
Verylowmdash
Ifspeciesndashhab
itat
basedon
qualitativeinform
ation(subjective
nature
mak
esitdifficu
ltto
quan
tify
certainty)
Highmdash
Statisticalsignificance
ofstressor
effectscanbeestimated
viaco
nfiden
ceintervalsof
param
eter
estimates
Moderateto
highmdash
Dep
endson
extent
towhichmod
elsareba
sedon
qualitativeversusem
piricalda
ta
Mod
eratemdash
Ifspecies
habitat
relation
shipsbasedon
empiricald
ata
Approachallowsuncertainty
ofrelation
shipsto
bequan
tified
and
inputinto
themod
els
Ran
geofstressors
evaluated
Highmdash
Not
limited
bysp
atial
dataon
stressors
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
andaccessible
spatiald
ata
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
and
accessible
spatiald
ata
Highmdash
Not
necessarily
limited
by
spatiald
ataon
stressorsifmod
els
based
onqualitativerelation
ships
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In more recent and complex approaches empirical speciesndashhabitat models are developed by sampling terrestrial speciesacross habitat types in a study area and using advanced statisticalanalyses to quantify species associations with different habitatsModels are termed resource selection functions (RSF) when spe-cies data (presenceabsence usedunused) are analyzed at smallwithin-population scales and species distribution models (SDM)or species abundance models (SAM) when data (occurrence abun-dance respectively) are analyzed at large range-level scales (Guisanand Zimmermann 2000 Guisan and Thuiller 2005 Elith andLeathwick 2009 Hegel et al 2010) For terrestrial birds current andemerging methods can be followed to convert species counts at asample station (the most common sample metric for terrestrialbirds) to a density estimate (Soacutelymos et al 2013 Matsuoka et al2014) Reporting species responses as densities (versus relativeabundance) provides (i) a relevant and readily communicatedmeasure of species response (ii) a standardized measure acrossregions and studies and (iii) an evaluation against conservationor management targets or population viability estimatesA range of statistical modeling frameworks are available to analyse
speciesndashhabitat relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) Typically habitat coefficients in these modelsare used to assign habitat rankings for species Both expert andempirically based habitat rankings require validation againstexternal data (or internal validations in the case of empiricalmodels) to ensure their reliability for an area (Pearce and Ferrier2000 Boyce et al 2002 Muir et al 2011) Once validated modelresults are used to assess the amount and distribution of suitablehabitat for species within a GIS (Millspaugh and Thompson 2009)Additionally when species responses have been converted to den-sity estimates coefficients can be coupled with GIS maps to esti-mate species densities or population sizes Within the context ofCEA the footprint or area of proposed projects existing projectsand disturbances and future land use scenarios can be overlaidon these maps to estimate the change in suitable habitat andassess effects on terrestrial species based on these habitat changeswithin the study area
322 Examples of applicationsWe highlight both project-based CEA and effects-based CEA
examples to demonstrate the flexibility of the habitat supplymethod Project-based CEAs typically use literature-based or hab-itat suitability models to identify map and quantify the area ofsuitable habitat for a given species within the study area We pro-vide a hypothetical example to explain how the habitat supplymethod is typically used to assess project-specific effects (Table 6)Changes in the amount of suitable habitat during the project(application phase eg from 10 to 6 of study area in Table 6)and after the project (closureremediation phase eg 0 changein Table 6) are estimated relative to current or baseline The hab-itat supply method is not typically used to assess CE because theextent to which existing or past stressors have affected pre-existinghabitat types is often not known Instead CE requirements areoften met by comparing the total area of the proposed project (eg5 of study area in Table 6) to the total area of other stressors regard-less of habitat affected (eg 35 Table 6) Thus in high or moder-ately disturbed areas these comparisons may suggest that a projectwould have minimal added effects relative to existing stressorsWhere data and resources allow some project-based CEAs mayuse historic maps or create hypothetical no-disturbance land-scapes within a GIS (eg see methods in ABMI 2017 Mahon et al2019) to assess how other stressors may have affected specific habi-tats However resource requirements (time data technical exper-tise) place this type of assessment beyond themandate or capacity ofmost project-based CEAs We include an example of the habitat sup-ply method from a recent project-based CEA from the IAAC RegistryT
able
4(conclud
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
inco
rpora
tetrad
itional
knowledge
Yes
Notusu
ally
Notusu
ally
Yes
Statistica
ltech
nical
expertise
required
Low
tomoderatemdash
Mod
eratewith
form
almod
eling
Moderate
High
Highto
very
high(specialized)
Tim
efra
me
Month
s
Month
sto
ltyea
rmdash
For
speciesndashhab
itat
mod
elsbased
onqualitativedata(dep
enden
ton
statusof
spatiald
ata
detailo
fthe
mod
ela
vailab
ilityof
species
inform
ation)
gtYea
rto
multi-ye
ar
Speciesdataco
llection
may
nee
dto
occu
rov
ergt1ye
arto
provide
sufficien
tsamples
Month
sto
gtmulti-ye
armdash
Highly
dep
enden
ton
sourceof
datainput
(existingdata
new
empirical
data
collection
)
Statisticalm
odel
dev
elop
men
tan
drefinem
entcanbetime
consu
ming
Modelingap
proachmay
require
additional
wee
ksor
month
s
gtYea
rmdash
Forem
piricalspe
ciesndashh
abitat
mod
elsthat
requ
irenew
data
collection
Dep
enden
ton
timerequired
toco
mpile
evaluatean
dcatego
rize
hab
itat
andstressor
data
Mahon and Pelech 209
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r pe
rson
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se o
nly
Tab
le5
Exa
mple
ofaqualitativereview
(matrixtable)for
aproject-based
CEA
Valued
compon
ent
Potential
effects
Mitigation
compen
sation
Residual
effect
Sign
ifican
ceof
residual
effects
Eco
logical
societal
value
Ove
rallsign
ifican
ceMag
nitude
Spatiale
xten
t
Freq
uen
cy
probab
ility
Duration
Rev
ersibility
Graywolf
Assoc
iatedwith
major
preysp
ecies
(moo
sed
eer)A
t
closure
increa
sed
area
sof
grasslan
d
willc
reatewhite-
tailed
dee
rhab
itat
andpoten
tially
higher
dee
ran
d
wolfnumbers
Limitfootprintof
disturbed
area
s
Increase
indeeran
d
wolfp
opulation
s
over
timeResidual
adverseeffect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
highqualitymoo
se
hab
itattherefore
limited
wolf
pop
ulation
MIN
IMAL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
is
long-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
is
reversibledu
ring
site
reclam
ation
Moo
sefollow
edby
wolvessh
ould
return
tosite
at
curren
tlevels
LOW
Wolvesarecommon
inthelocaland
region
alstudy
areasLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
wolve
s
aremob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
American
black
bea
r
Initialm
igration
duringsite
preparationan
d
constru
ction
return
tosite
area
afterperiodof
human
noise
hab
ituation
Limitfootprintof
disturbed
areas
Establishwildlife
policyto
minim
ize
human
interaction
withwildlifean
d
decrease
poten
tial
forhab
ituation
Increa
sedseason
al
use
ofthesite
over
timedep
endson
succession
of
reclaimed
area
s
Residual
adve
rse
effect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
abov
eav
erag
ebea
r
hab
itatM
INIM
AL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
islong-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
reversibledu
ring
site
reclam
ation
New
grasslan
d
hab
itat
may
increase
the
season
alhab
itat
use
bybe
ars
LOW
Bearde
nsity
inthe
study
site
islikely
comparab
leto
that
inthesurrou
nding
landscapeLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
bea
rsare
mob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
NoteT
hetable
summarizes
theproposed
project
effectsmitigationan
dco
mpen
sation
mea
surespoten
tial
residual
effects(theen
vironmen
tale
ffectof
aproject
that
remainsaftermitigationmea
sureshav
ebee
nim
plemen
ted)sign
ificance
ofresidual
effectsin
considerationof
ava
rietyof
assessmen
tfactors(ratings
inboldface
type)a
ndwhether
asign
ificantad
verseen
vironmen
tale
ffecton
ava
lued
compon
entisex
pected
asaresu
ltof
project
implemen
tation
Sou
rceEnvironmen
talImpactStatem
entfortheMarathon
PGM-CuProject20
12Thetable
represents
description
san
dratings
summarizingtheen
vironmen
tale
ffects
ofametal
minedev
elop
men
ton
twova
lued
compon
ents
(graywolfan
dAmerican
black
bea
r)
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(Environmental Impact Statement for the Springbank Off-streamReservoir Project 2018) to estimate area changes in the amountof suitable habitat (based on habitat suitability models) for selectVCs Spraguersquos Pipit (Anthus spragueii) and Northern Leopard Frog(Lithobates pipiens) during different flood scenarios created by waterdiversion to a channel and reservoir within the proposed projectarea (Table 7)Effects-based CEAs typically involve the creation and application
of empirical speciesndashhabitat models within the study area Maceet al (1999) evaluated the CE of human and resource activity ongrizzly bear (Ursus arctos) habitat inwesternMontana during springsummer and fall seasons The authors modelled bear resourceselection from telemetry data satellite imagery elevation humanactivity points roads and trails and extrapolatedmodels to a largerregion with similar climate and vegetation Quantifying and map-ping areas with a high probability of use by bears in each seasonwas used to inform conservation and management actions (egroad or trail closures within important seasonal habitats habitatrestoration future road creation) Roumldder et al (2016) outline aninnovative approach to integrate SDM and potential connectivitymodels to identify connected and isolated populations and habitatcorridors for a species of conservation concern the Sand Lizard(Lacerta agilis) in western Germany (Fig 2) Quantifying potentialconnectivity areas (for movement like dispersal or migration) wasused to identify areas of strong connectivity persistent isolationand stable connective networks needed to connect populationsThis proposed framework may be particularly useful for proposeddevelopments that affect (i) portions of a previously larger inter-connected population or (ii) connective features between perma-nently colonized habitat patches Additional examples from thepublished literature are listed in Table 3
323 Advantages and limitations CEA questions and whenwhyto use3231 AdvantagesAdvantages of the habitat supply method include its ability to
quantify effects using loss of suitable habitat This approach
which has wide-spread application in wildlife research may besuitable when (i) data to map and measure habitat change areavailable but empirical data on species response to specific stres-sors are limited (ii) collaborative approaches focused on localissues and values are important (eg speciesndashhabitat associa-tions may be based on both qualitative (TK local and expertknowledge) and quantitative (speciesndashhabitat models) data) and(iii) data to develop empirical speciesndashhabitat models and adjusteddensity estimates are available so that total impacts can be com-pared to current and future population targets (eg Mahon et al2014)
3232 LimitationsLimitations to the habitat supply modelling approach include
the availability of accurate publicly available spatial data forhabitats and stressors and reliable specieshabitat associationsfrom the region of interest If additional data are required torefine existing habitat suitability models or create new empiricalspeciesndashhabitat models within a study area stakeholders or part-ners should a priori develop rigorous sampling designs and sur-vey protocols to meet project objectives We note two additionalcautions when using this approach Specieshabitat associationsbased on limited empirical data from within the region or datafrom outside the region require model validation and testing(Muir et al 2011) Finally stressor effects are limited to how stres-sors contribute to the loss of suitable habitat (habitat changesare used to infer population consequences) Habitat-based analy-sis alone may not be useful because (i) habitat is assumed to bea useful measure of population status (ii) habitat needs of the tar-get species or VC are assumed to be known (Schultz 2010) and(iii) specific stressor effects on the target species or VC are notquantified (eg the shape magnitude and direction of speciesrsquoresponse to stressors indirect or novel effects of stressors on speciesor interactions among stressors) Failure to identify which stressorsare influencing target species or VCsmay limit future research intobiological mechanisms (eg habitat selection movement disper-sal reproduction survival predatorndashprey dynamics) needed to
Fig 1 Example of a qualitative review (conceptual modeldiagram) for an effects-based CEA Figure represents an annual life cycle ofevents of Greater Sage-Grouse responding to oil and gas development in southwest Wyoming USA (from Heinrichs et al 2019 reproducedwith permission of Ecol Appl Vol 29 copy2019 Ecological Society of America)
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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
218 Environ Rev Vol 29 2021
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al u
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
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Austin M 2007 Species distribution models and ecological theory a criticalassessment and some possible new approaches Ecol Modell 200 1ndash19 doi101016jecolmodel200607005
Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
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Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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iron
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ded
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om b
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AT
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AC
IFIC
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NA
TL
LA
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n 12
07
21Fo
r pe
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al u
se o
nly
Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
Canadian Environmental Assessment Agency (CEAA) 2015 Assessing Cumula-tive Environmental Effects under the Canadian Environmental AssessmentAct 2012 March 2015 Report Available from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
Canadian Environmental Assessment Agency (CEAA) Expert Panel Review 2017Building common ground a new vision for impact assessment in Canada Thefinal report of the expert panel for the review of Environmental assessmentprocesses Available from httpswwwcanadacaenservicesenvironmentconservationassessmentsenvironmental-reviewsenvironmental-assessment-processesbuilding-common-groundhtml [last accessed 10 April 2020]
Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
Environmental Impact Statement for the Springbank Off-stream ReservoirProject 2018 Environmental Impact Assessment of the Springbank Off-stream Reservoir Project Stantec Incorporated Available from httpsiaac-aeicgcca [last accessed 15 June 2020]
Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
Impact Assessment Agency of Canada (IAAC) 2019 The Impact AssessmentProcess Timelines and Outputs Available from httpswwwcanadacaenimpact-assessment-agencyhtml [last accessed 20 August 2020]
Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
Johnson C 2016 Understanding the spatial responses of caribou to human-caused disturbance In Development of modeling tools to address cumulativeeffects on the summer range of the Bathurst caribou herd ndash a demonstrationproject Edited by JS Nishi and A Gunn Environment and Natural ResourcesGovernment of the Northwest Territories Yellowknife NT pp 13ndash36
Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
Noble B Liu J and Hackett P 2017 The contribution of project environmentalassessment to assessing and managing cumulative effects individually andcollectively insignificant Environ Manage 59 531ndash545 doi101007s00267-016-0799-7 PMID27885387
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Pearce J and Ferrier S 2000 Evaluating the predictive performance ofhabitat models developed using logistic regression Ecol Modell 133(3)225ndash245 doi101016S0304-3800(00)00322-7
Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
Salice CJ 2012 Multiple stressors and amphibians Contributions of adversehealth effects and altered hydroperiod to population decline and extinctionJ Herpetol 46(4) 675ndash681 doi10167011-091
Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
Sawyer H Kauffman MJ and Nielson RM 2009 Influence of well pad ac-tivity on winter habitat selection patterns of Mule Deer J Wildl Manage73(7) 1052ndash1061 doi1021932008-478
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Mahon and Pelech 223
Published by NRC Research Press
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Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
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Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
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224 Environ Rev Vol 29 2021
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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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precipitation change permafrost melt) and climate-inducedchanges to natural disturbance regimes (eg wildfire floods insectoutbreaks disease outbreaks) resulting from human-caused cli-mate change are also influencing landscapes and habitats (Stralberget al 2018) Climate stressors may combine with existing resourceand land use stressors to create novel and complex additive or inter-active effects (Didham et al 2007 Darling and Cocircteacute 2008) Theseindividual and combined stressors alter the quantity and quality ofnatural habitats Understanding the cumulative effects of stressorsis challenging because of the complexity of ecosystems the vari-ability of stressors and species response to individual or multiplestressors (Hodgson et al 2019 Mahon et al 2019)Evaluating the combined or cumulative effects of these human-
induced disturbances (hereafter stressors) on terrestrial species isnow central to many conservation and management (Gunn et al2014) research and monitoring (Burton et al 2014 Mahon et al2019) and land use planning (Johnson et al 2011 Chetkiewicz andLintner 2014 BC Ministry of Forests Lands and Natural ResourcesOperations 2016 Yukon Land Use Planning Council 2018) efforts inCanada Here we define cumulative effects (CE) as the combinedeffects of multiple stressors on species or ecosystems over timeand (or) space and stressor as all human-induced activities anddrivers We identify two key types of cumulative effects assess-ments (hereafter CEAs) within Canadian jurisdictions The firstare project-based CEAs (Dubeacute and Munkittrick 2001 Noble 2010)initiated as part of individual project environmental assessments(hereafter EA) These can include assessments within an Environ-mental Impact Statement to meet requirements of the CanadianEnvironmental Assessment Act 2012 or within an Impact Statement tomeet requirements of the Impact Assessment Act 2019 The second areeffects-based or regional CEAs (Dubeacute and Munkittrick 2001 Noble2010) initiated as (i) one component of Regional Assessments(Impact Assessment Act 2019) which are studies and analyses conductedby an appointed committee or by the Impact Assessment Agencyof Canada (IAAC) in areas of existing or proposed projects ldquotoinform planning and management of CEAs and inform projectimpact assessmentsrdquo (IAAC 2019 httpswwwcanadacaenimpact-assessment-agencyhtml) or (ii) regional projects or studies conductedby partners or groups within a region of interest (eg land useplanning region) Both types of CEAs occur within Canadianjurisdictions but project-based and effects-based CEAs differ intheir goals and objectives We note differences between thescience-based goal of most effects-based CEAs (to characterizeanalyze and quantify individual and multiple stressor effectson species and ecosystems within a region) and the more limitedgoal of project-based CEAs (to undergo project review and approvalwithin a defined regulatory process Duinker and Greig 2006Noble 2010 Gunn and Noble 2011 Westwood et al 2019) Largerregional scale effects-based CEAs are needed to quantify the CE ofmultiple stressors on species and ecosystems where diverse sectors(eg in terrestrial systems sectors like forestry agriculture energymining) and processes (climate change) operate simultaneously(spatially temporally) within a region Limitations of project-basedCEAs to assess CE at larger regional scales have been widelyreviewed (Duinker and Greig 2006 Gunn and Noble 2011 Gillinghamet al 2016 Jones 2016) and include (i) insufficient spatial scale andscope to assess a range of stressors (ii) limited resources and dataand (iii) uncoordinated inconsistent and fragmented individualassessmentsThe long history and regulatory requirements of project-based
CEA have generated a large body of examples federal and juris-dictional CEA frameworks technical guidance documents andscholarly discussion Notably lacking however is specific scien-tific guidance that describes and evaluates analytical methods(eg qualitative and quantitative analysis methods) that can beused for both project-based and effects-based CEAs where thefocus is on terrestrial species and systems Here qualitative refersto the assignment of a value (eg low or high) or ranking for a
variable of interest and quantitative refers to the measurementof a quantity for a variable of interest Regulatory guidance docu-ments and published reviews suggest CEA methods that rangefrom qualitative approaches like questionnaires interviewschecklists matrices and diagrams to quantitative approacheslike habitat suitability models species stressor models and sim-ulation models (CEAA 2014 2018 Cocklin et al 1992 Smit andSpaling 1995 Schultz 2010 Canter and Atkinson 2011 Duinkeret al 2013) but offer little guidance regarding when or why touse specific methods and which CEA questions can be addressedWhile this generality and flexibility is understandable given CEAguidance must cover a range of projects regions biophysical com-munities and valued components (environmental componentsconsidered most indicative critical or important to ecosystemservices or stakeholder values hereafter VC) it can also givethe false impression these methods are equally effective inanswering many key science-based CEA questions For exam-ple one of the most frequently cited rationales for undertakingCEA in both project-based and effects-based CEAs is the poten-tial for multiple stressors to create unexpected additive orinteractive effects (Didham et al 2007 Crain et al 2008 Darlingand Cocircteacute 2008) However as many CEA methods are based onexisting data or expert opinion very few are capable of testingfor novel or complex effectsHere our objective is to review and provide guidance on quali-
tative and quantitative CEAmethods for species in terrestrial sys-tems relevant to both project-based CEAs and effects-based CEAsWe focus on terrestrial systems and species because althoughsome methods are applicable to both terrestrial and aquatic sys-tems some quantitative tools necessary to facilitate CEA meth-ods (eg marine spatial planning tools multi-species models)have been specifically developed for use in marine or aquatic sys-tems In addition although recent reviews of CEA methods havebeen conducted in aquatic systems (see Hodgson and Halpern2019) no similar reviews have been conducted for terrestrial sys-tems and species Specifically we discuss four broad categories ofmethods with current or potential use for project-based andeffects-based CEAs (i) qualitative review (ii) habitat supply mod-els (iii) empirical speciesndashstressor models and (iv) decision sup-port models We describe each method and provide exampleshighlight advantages and limitations identify how methods addresskey science-based CEA questions and provide direction on whenand why to use specific CEA methods The larger scale need forpolicy shifts and associated mechanisms to promote the imple-mentation of methods reviewed here (ie coordinated collabo-rative regional CEAs) are beyond the scope of this documentand have been well reviewed elsewhere (eg Gillingham et al2016 Jones 2016 Hodgson et al 2019 Westwood et al 2019)Our intended audience includes current CEA practitioners
however we direct this document to biologists planners andpolicy makers new to CEA or considering CEA outside of regula-tory project-based CEA We expect many current practitionersespecially biologists conducting assessments will be familiarwith the methods we outline here Thus while we hope our dis-cussion helps consolidate and synthesize information for thoseindividuals we particularly hope it aids discussions with managersand policy makers to ensure terrestrial CEA frameworks allowfor rigorous design and analysis We structure our discussionreviewas follows (i) goals questions and objectives of CEA (ii) review ofCEAmethods and (iii) conclusions and key recommendations
2 Goals objectives and questions related tocumulative effects assessment (CEA)We acknowledge that goals and objectives for project-based
and effects-based CEAs differ because regulatory CEAs specific toCanadian jurisdictions differ from nonregulatory CEAs (Table 1)Project-based CEAs within provincial jurisdictions initiated before
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28 August 2019 follow technical guidance associated with theCanadian Environmental Assessment Act 2012 (CEAA 2014 2018)while those initiated after 28 August 2019 follow technical guid-ance associated with the Impact Assessment Act 2019 (IAAC 2019)Critical to the CEAA technical guidance (CEAA 2014 2018) andoperational policy statement (CEAA 2015) is identifying meas-ures that would mitigate adverse CE (step 3) and determiningthe significance of any adverse CE taking into account theimplementation of mitigation measures (step 4) Similarly newIAAC technical guidance (see httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancesection-22-factors-considered-descriptionshtml) requires the consideration of anyCE (section 22 (a) (ii)) and any measures that would mitigate anyadverse effects of the designated project (section 22 (a) (b)) Project-based CEAs within northern jurisdictions (Yukon TerritoryNorthwest Territories Nunavut) follow specific technical guidance(Table 1) The focus of most project-based CEAs in Canada is toidentify and mitigate the individual stressors associated with theproposed project This process involves determining whetherproject activities in the context of other past present or futureactivities will cause significant adverse CE after the implementationof proposedmitigationmeasuresEffects-based CEAs conducted as part of Regional Assessments
under the IAAC are studies conducted in areas of existing proj-ects or anticipated projects within provincial jurisdictions toinform planning and management of CE and inform projectimpact assessments The Minister of Environment and ClimateChange appoints a committee or asks the IAAC to conduct a Re-gional Assessment establishes the terms of reference for eachRegional Assessment and reviews the final Regional Assessmentreport Regional Assessments may differ in their goals activitiesand approaches that can include filling data gaps and analyzing
trends establishing thresholds and standard mitigation or iden-tifying regional development objectives and scenarios (httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidanceregional-assessment-impact-assessment-acthtml) We notethat Regional Assessments under the IAAC are a new process(two Regional Assessments have been initiated and only oneRegional Assessment has been completed as of November 2020)Specific guidance and direction would ensure that future RegionalAssessments meet objectives of planning assessing and managingCE Regional Assessments under the IAAC have the potential to bea valuable and useful process for assessing CE in a region usinginnovative methods approaches and tools We promote the useof rigorous CEA analytical methods within Regional Assessments to(i) quantify current and future impacts of human activities andclimate change (ii) manage where when and how human activityoccurs and (iii) produce alternate scenarios to balance humanactivity and species conservationEffects-based CEAs outside of regulatory processes can be con-
ducted in any jurisdiction in Canada by interested partnersgroups or agencies (eg academic institutions provincial or ter-ritorial government agencies environmental nongovernmentalagencies Indigenous communities or groups research groupsland use planning commissions) The focus of most nonregula-tory effects-based CEAs in Canada is often on assessing and ana-lyzing stressor-effect relationships and simulating land andresource development scenarios within a specific region of inter-est This typically involves quantifying regional effects of stres-sors often associated with multiple sectors identifying types ofstressor combinations measuring uncertainty and (or) simulat-ing the impacts of stressors on habitats and terrestrial speciesinto the future using various landscape climate resource ormanagement scenarios
Table 1 Types of cumulative effects assessment (CEA) within Canada
Type of CEA Jurisdiction Agency Guidance CEAmethodsapproach
Project-based CEA1 CEA within EAIA Provinces CEAA Examine cumulative effects (CE) of a project on VCs usingfive steps (1) scoping
IAAC Examine CE (changes to environment health social and economic conditions)usingfive steps preceding (interim guidance httpswwwcanadacaenimpact-assessment-agency CEAA 2014 2018)
YT YESAB Examine CE onVCs by considering project effects (magnitude duration timinglikelihood reversibility and spatial extent) and other past current or futureproject effects (httpswwwyesabca YESAB 2019)
NT MVEIRB Examine CE using four steps (1) identifying parts of the environment affectedby project (2) determining parts of the environment affected by other pastpresent or future projects (3) predicting effects of proposed project andother projects (4) identifying ways tomanage combined impacts (httpsreviewboardca MVEIRB 2004)
NU NIRB Identify and document cumulative impacts from the project and any otherpast current or future project and determine whether project is of regionalinterest (httpswwwnirbca NIRB 2020)
Effects-based CEA1 Regional Assessment Provinces IAAC Examine CE using methods identified by the appointed committee or the
IAAC and documented in the terms of reference2 Regional Projects Any location or
Examine CEwithin region of interest using questions andmethods defined bypartners
Note Jurisdiction definitions mdash YT Yukon Territory NT Northwest Territories NU Nunavut Agency definitions mdash CEAA Canadian Environmental AssessmentAgency IACC Impact Assessment Agency of Canada administers projects under Canadian Environmental Assessment Act 2012 and Impact Assessment Act 2019 YESABYukon Environmental and Socio-economic Assessment Board administers projects under Yukon Environmental and Socio-economic Assessment Act 2003 MVEIRBMackenzie Valley Environmental Impact Review Board administers projects underMackenzie Valley Resource Management Act 1998 NIRB Nunavut Impact Review Boardadministers projects under Nunavut Planning and Project Assessment Act 2013 partners can be academic institutions provincial or territorial government agenciesenvironmental non-governmental agencies Indigenous communities or groups research groups land use planning commissions Acronyms EA environmentalassessment IA impact assessment VC valued components
Mahon and Pelech 203
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There are multiple CEA definitions frameworks (see Duinkeret al 2013) processes and questions (as described previously andin Table 1) In this review we define key science-based CEA objec-tives relevant to both project-based and effects-based CEAs as thefollowing (i) describe or characterize overall CE (ii) determinethe rank or magnitude and direction (eg positive or negative) ofindividual stressor effects (iii) describe the shape of the relation-ship between stressors and species (eg linear or nonlinear)(iv) determine how stressors combine to affect species (eg addi-tive interactivendashsynergistic interactivendashantagonistic compensa-tory or masking) (v) relate CE to baseline or pre-disturbanceconditions defined thresholds or management triggers and(vi) describe or assign certainty or probability of occurrence ofpredicted effects Within our review we assess the capacity ofCEA methods for species in terrestrial systems to address thesescience-based CEA objectives
3 Description and evaluation of CEAmethodsHere we provide an overview of current methods for project-
based and effects-based CEAs that have or could be used for terres-trial species We conducted a three-stage review to (i) identify CEAmethods used in project-based CEAs and effects-based CEAs and(ii) document relevant examples First we reviewed all active assess-ments (as of 15 June 2020) listed on the Canadian Impact AssessmentRegistry (httpsiaacgcca) including Environmental Assess-ments under Canadian Environmental Assessment Act 2012 (n = 50)Environmental Assessments by Review Panel under CanadianEnvironmental Assessment Act 2012 (n = 8) Impact Assessmentsunder Impact Assessment Act 2019 (n = 2) and Regional Assessmentsunder Impact Assessment Act 2019 (n = 2) and recorded the types ofmethods used within the CEA (eg Environmental ImpactStatement Impact Statement Regional Assessment) in Table 2Second we searched for published reviews of CEA methods
within the scientific literature and the CEA guidance literature(Table 2) We collapsed similar approaches identified in stages 1and 2 into four broad categories of CEA methods qualitativereview habitat supply models empirical speciesndashstressor modelsand decision support models (focusing on Bayesian belief network(BBN) models) We distinguished data products like indicators (iea measurable variable chosen to represent the state of a compo-nent) and indices (ie composite numerical value for some envi-ronmental attribute usually comprised of two or more variables)that can be used within a CEA process from analytical CEAmethods We also distinguished data tools like geographic in-formation systems (GIS) used to produce spatial data productsincluded within most modern project-based or effects-basedCEAs from analytical CEAmethodsThird we conducted targeted searches using Web of Science
across the four CEA methods to find published examples for spe-cies in terrestrial systems We include some CEA publishedpapers as examples in the description of each CEAmethod in sub-sequent sections and list all papers reviewed by CEA methodincluding study area location and target terrestrial species orgroup (Table 3) CEA methods fall on a gradient of increasingcomplexity data requirements and technical expertise In manycases a combination of CEA methods will be required to effec-tively answer science-based CEA questions Our intent is not anexhaustive review of all CEA methods or an intensive review ofone category of methods Instead we describe each CEA methodand provide relevant examples We also identify advantages andlimitations explain how methods address science-based CEAquestions (Table 4) and provide direction on when and why touse specific CEAmethods
31 Qualitative review
311 Description of methodThis broad category of methods includes a variety of approaches
to collect (questionnaires interviews workshops literature reviews)
and summarize (checklists matrices qualitative or conceptualmodelsdiagrams) qualitative data or information (eg expertopinion published or unpublished information) as an exclusiveor primarymeans to assess CE (Cocklin et al 1992 Smit and Spaling1995 Duinker et al 2013 CEAA 2014 2018 see Tables 2 and 3)rather than direct measurements of species response to habitatchange or stressors This approach would typically include assess-ments with high constraints on data availability (for species habi-tats and stressors) time and (or) cost For example informationon stressors may be based on qualitative descriptions or approxi-mations of development (eg hard copies of maps written orverbal descriptions of proposed developments or stressors) ratherthan spatial and attribute data on stressors within a GIS In somecases qualitative datainformation is ranked to provide a categori-cal assessment of effects (eg using literature or expert review torankweight potential effects of stressors on species) Formal con-sensus techniques and qualitative modelling can also be used (egDelphi method see Linstone and Turoff 2002 fuzzy cognitivemapping see Gray et al 2015) although these remain infrequentfor most assessments of terrestrial species Although also a formof qualitative review we consider independent traditional knowl-edge (TK) studies or combined TK and scientific knowledge (SK) stud-ies (eg co-production of knowledge) to be separate from thiscategory due to distinct methods used to select experts with dif-ferent types of knowledge (eg local harvesters and huntersElders government employees scientists) and conduct the elici-tation procedure (Huntington 1998 Usher 2000 Polfus et al 2014Beacutelisle et al 2018)Qualitative review is common among project-based CEAs for
terrestrial species (see Table 2 stage 1) where proponents typi-cally conduct literature reviews to collect information and usechecklists and matrices to rank potential effects on VC (eg lowto high) Qualitative review is less common among effects-basedCEAs for terrestrial species (outside of TK studies) with the excep-tion of (i) potential stressor effects that are difficult to map orquantify (eg pesticide use pollution species interactions) and(ii) complex systems where multiple stressor effects are difficultto visualize or comprehend In the latter case conceptual modelsdiagrams can be combined with other quantitative CEA approaches(eg habitat supply empirical speciesndashstressor and decision supportmodels) where the conceptual model is shared refined and usedto guide monitoring and research questions and hypothesesamong partners (Marcot et al 2006 Lindenmayer and Likens 2010Nelitz et al 2015)
312 Examples of applicationsWe highlight two examples of qualitative review methods and
list additional examples from the published literature in Table 3The first example from a recent project-based CEA from theIAAC Registry (Environmental Impact Statement for the MarathonPGM-Cu Project 2012) uses a matrix table (Table 5) to identify theVCs potential effects mitigation measures and residual effectsof the project For each VC gray wolf (Canis lupus) and Americanblack bear (Ursus americanus) a rank or level is assigned to eachparameter used to characterize residual effects (magnitude spa-tial extent frequency duration reversibility ecological or soci-etal value) These levels are then combined to create a measureof overall significance a required first step in conducting theCEA (CE are defined as the environmental effects resulting fromthe project in combination with other projects or activities) Thesecond example from an effects-based CEA uses a conceptualmodeldiagram to illustrate the Greater Sage-Grouse (Centrocercusurophasianus) annual life cycle including influence of oil and gasdevelopment on site fidelity dispersal and demography (Fig 1)The life cycle informs the population simulation model used totrack population dynamics and habitat use of simulated Sage-Grousethrough space and time as they respond to changing landscape
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conditions (climate-induced vegetation change) and exposure tooil and gas stressors (Heinrichs et al 2019)
313 Advantages and limitations CEA questions and whenwhyto use
3131 AdvantagesThe advantage of qualitative review is its utility when quantita-
tive data on habitat and stressors and time and (or) financialresources are limited The approach also has the ability to (i) offera collaborative flexible and accessible approach when localissues and values are important (eg TK or combined TK and SKstudies) (ii) identify a wide range of stressors and mechanisms
(eg indirect effects species interactions stressors that cannotbe effectively mapped) and (iii) serve as a conceptual frameworkwithin a CEA using one or more quantitative methods to assessand (or) simulate multiple stressor effects (eg empirical speciesndashstressor models Bayesian belief network (BBN) models simulationmodels)
3132 LimitationsThe limitation of qualitative review is uncertainty surrounding
the objectivity and reliability of assessments particularly whenexpert opinion is applied to novel locations or stressors Specifi-cally these limitations include the inability to quantify any stres-sor related impacts including effects on habitat area (eg amount
Table 2 Cumulative effects assessment (CEA) methods identified in the stage one review (Canadian Impact Assessment Registry) and the stagetwo review (published reviews of CEAmethods and publicly available CEA guidance documents)
Stage CEAmethods
Stage 1 Canadian Impact Assessment Registry Total 2262 (35) with Environmental Impact Statement ImpactStatement Regional Assessment Report
Environmental Assessment under Canadian Environmental Assessment Act2012 (n = 50)
1650 (32) with Environmental Impact Statement1516 used QR416 used QR and HSM
Environmental Assessment by Review Panel under CanadianEnvironmental Assessment Act 2012 (n = 8)
58 (625) with Environmental Impact Statement55 used QR35 used QR and HSM15 used QR HSM and SM
Impact Assessment under Impact Assessment Act 2019 (n = 2) 02 (0) with Impact StatementRegional Assessment under Impact Assessment Act 2019 (n = 2) 12 (50) with Regional Assessment Report
1 used QR HSM and DSM
Stage 2 Reviews of CEAmethodsCocklin et al 1992 Environmental checklists matrix approaches inputndashoutputmethods
approaches including network analysisCanter and Atkinson 2011 Indicators and indices habitat suitability modelsDuinker et al 2013 GIS scenarios thresholds indicators and indices network analysis
matrices habitat suitability modelshabitat selection modelshabitat supply models stressor-based and effects-based approachesmultivariate statistical analysis air pollutionmodels landscapemodels animal population simulation models water-basedsimulationmodels public engagement community-basedmonitoring population viability analysis costndashbenefit analysisvisual amenity analysis traffic-light decision support systemsustainability appraisal
Hodgson and Halpern 2019 Experiment meta-analysis single species models mappingqualitative models multi-species models (aquaticmarine)
Marcot et al 2001 Marcot et al 2006 McCann et al 2006 Uusitalo 2007 Bayesian networkmodelsBayesian belief network (BBN) models
Stage 2 CEA guidance documentsTechnical Guidance for Assessing CEA under Canadian EnvironmentalAssessment Act 2012 Draft (CEAA 2014) Technical Guidance forAssessing CEA under Canadian Environmental Assessment Act 2012InterimGuidance (CEAA 2018)
Questionnaires and interviews checklists and matrices network andsystems analysisdiagrams indicators and indices conceptual andnumerical models trend analysis spatial analysis
Section 22-Factors to be considered (IAAC 2019 httpswwwcanadacaenimpact-assessment-agency)
New IAAC guidance in preparation interim guidance is CEAA (2018)
Consideration of cumulative effects in YESAB Assessments (YESAB 20052019)
No CEAmethods
Environmental Assessment Guidelines (MVEIRB 2004) No CEAmethodsProponents Guide NIRB Technical Guide Series (NIRB 2020) No CEAmethods
Note Agency definitions CEAA Canadian Environmental Assessment Agency IACC Impact Assessment Agency of Canada administers projects under CanadianEnvironmental Assessment Act 2012 and Impact Assessment Act 2019 YESAB Yukon Environmental and Socio-economic Assessment Board administers projects underYukon Environmental and Socio-economic Assessment Act 2003 MVEIRB Mackenzie Valley Environmental Impact Review Board administers projects underMackenzie ValleyResource Management Act 1998 Nunavut Impact Review Board (NIRB) administers projects under Nunavut Planning and Project Assessment Act 2013 CEA methoddefinitions QR qualitative review HSM habitat supply models SM simulation models DSM decision support models
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of suitable habitat lost) and direct (eg habitat loss mortality) orindirect stressor effects on species (eg altered habitat selectionreduced demographic rates like reproduction or survival)
3133 Science-based CEA questionsSee Table 4 to identify how qualitative review methods address
key science-based CEA questions
3134 Whenwhy to usemdash project-based CEAsldquoWhen to userdquo when quantitative data on species and spatial
data on habitats or stressors limit the development of speciesndashhabitatmodels (to assess change in habitat) and (or) speciesndashhabitatndashstressor models (to assess species response to habitat and stressors)Qualitative data and qualitative reviewmethods can be used to iden-tify rank and categorize stressor effects ldquoWhy to userdquo allows for acomprehensive listing and ranking of potential stressor effects onVCs in relation to (i) mitigation measures for the proposed projectand (ii) other past current or future projects
3135 Whenwhy to usemdash effects-based CEAsldquoWhen to userdquo when systems are complex conceptual diagrams
can be combined with other quantitative methods and used to(i) identify multiple stressors and (or) pathways of effects on dif-ferent life stages of target species or VCs (see Fisher and Burton2018 Pirotta et al 2018 and Heinrichs et al 2019 for examples)and (ii) outline the analysis process or framework (see Munns2006 and Katzner et al 2020 for examples) ldquoWhy to userdquo allowsfor a visual representation of the system (stressors ecologicaldrivers individual and population responses) and the analysisprocess
32 Habitat supply models
321 Description of methodIn this category we include various forms of speciesndashhabitat
modelling currently the most common approach to terrestrialspecies assessment in most project-based CEAs as well as in con-servation planning and landscape management (eg Morrisonet al 2006) Approaches vary in complexity from simple literature-based habitat rankings to complex statistical models of empirical
speciesndashhabitat relationships (see Table 3) A defining characteristicof this method for CEA is that habitat is used as a surrogate for theidentified terrestrial species (or as a VC itself) For habitat supplymodels quantitative changes in the amount and (or) quality of aspeciesrsquo habitat (typically categorized and measured within a GIS)are used as the primary measure of CE In contrast for empiricalspeciesndashstressormodels the species direct response to one ormorestressors is used as the primary measure of CE We provide morescientific background to this approach subsequently however ingeneral this approachwith respect to CEA typically involves
1 Developing speciesndashhabitat associations or models Thisrequires ranking the suitabilityeffectivenessfunctionality ofmeasurable habitat categories for target species using expertopinion literature review and (or) empirical speciesndashhabitatcorrelations
2 Quantifying changes in the amount of this habitat as a resultof the proposed project or projects and existingfuture stressors
3 Inferring effects on species as a result of these habitat changes
Speciesndashhabitat models can be developed using a variety ofexpert opinion and statistical modelling approaches have a longhistory in wildlife management and landscape planning and arewell-documented in the literature (eg Morrison et al 2006Millspaugh and Thompson 2009 Guisan et al 2013) Standardsfor developing literature and expert opinion based habitat suit-ability indices (HSI) date back to the 1980s (US Fish and WildlifeService (USFWS) 1980 1981) and many jurisdictions have simi-larly developed protocols for wildlife habitat rankings in Canada(BC Resource Inventory Committee 1999 Clarke 2012) Thesehabitat suitability models typically use experts and (or) compilespecies information from existing literature to rank habitats fora species (eg on a 0ndash1 scale or similar categorical ranking sys-tem) Additional variables that may modify habitat quality (egproximity to roads) are also often considered to differentiatebetween habitat capability (the potential of the habitat to sup-port the species under optimal conditions) and habitat suitabilityor effectiveness (the actual ability of the habitat to support the spe-cies in a specific study area)
Table 3 Cumulative effects assessment (CEA) methods identified in the stage three review (targeted reviews of CEA methods) qualitativereview habitat supply models empirical speciesndashstressor models and decision support models (Bayesian belief network (BBN) models)
Stage 3 Targeted reviews of CEAmethods References
Qualitative review Chilima et al 2013 (Ontario CAN watersheds) Cooper 2011 (United Kingdom multiple species)Swor and Canter 2011 (Pennsylvania USA water fish mussels riparianfloodplain resources)
Habitat supply models Canter and Atkinson 2011 (USA multiple species) Houle et al 2010 (Quebec CAN gray wolf)Johnson et al 2005 (Northwest Territories CAN caribou gray wolf grizzly bear wolverine) Maceet al 1999 (Montana USA grizzly bear) Roumldder et al 2016 (Cologne Germany Sand Lizard)
Empirical species-stressor models Sawyer et al 2009 (Wyoming USA mule deer) Houle et al 2010 (Quebec CAN gray wolf) Salice et al2011 (South Carolina USA Eastern Narrow-mouthed Toad) Salice 2012 (southeast USA EasternNarrow-mouthed Toad) Wilson et al 2013 (northwest Alaska USA caribou) Goodale andMilman2019 (offshore regions waterfowl waterbirds landbirds) Hodgson et al 2017 (various locationsinsects birds fish ungulates) Jones et al 2017 (Muskoka RiverWatershed CAN benthic invertebrates)Fisher and Burton 2018 (northeast Alberta CAN gray wolf white-tailed deermoose American blackbear coyote Canada lynx fisher red fox snowshoe hare American red squirrel) Claireau et al 2019(western France 10 bat species) Daniel and Koper 2019 (southern Alberta CAN 5 grassland landbirdspecies) Heinrichs et al 2019 (Wyoming USA Greater Sage-Grouse) Mahon et al 2019 (northeastAlberta CAN 27 boreal landbird species) Katzner et al 2020 (California USA roadrunner hawk)Leston et al 2020 (northeast Alberta CAN 20 boreal landbird species)
McNay et al 2006 (north-central British Columbia CAN caribou) Steventon et al 2006 (north coastof British Columbia CAN Marbled Murrelet) Howes et al 2010 (eastern Australia woodlandlandbirds) MacCracken et al 2013 (Bering and Chukchi Seas Pacific walrus) Ban et al 2014 (GreatBarrier Reef Australia reef coral) Fortin et al 2016 (Alaska USA brown bears) Mantyka-Pringleet al 2016 (South East Queensland Australia macroinvertebrates fish) Mantyka-Pringle et al 2017(Slave River Delta Northwest Territories CAN fish waterfowl mammals)
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Tab
le4
Summaryco
mparison
ofan
alytical
method
sforcu
mulative
effectsassessmen
t(CEA)for
terrestrialspecies
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Cumulative
effectsof
stressors
bas
edon
Exp
ertknow
ledge
literature
review
oflikelyeffects
Estim
ated
chan
gein
amou
ntof
suitab
lehab
itat
forsp
eciesas
aresu
ltof
stressorssu
itab
lehab
itat
isch
arac
terize
dusingex
pert
opinionliterature
and(or)
empirical
data
Directmea
suremen
tsof
speciesrsquo
resp
onse
tograd
ientof
stressors
andco
mbinationsof
stressors
Mod
eled
respon
seof
speciesab
unda
nce
orhab
itat
tocombined
hab
itatstressor
andother
inpu
ts(in
putsmay
bequ
alitativeor
empiricald
ata)
Most
commonly
asso
ciated
with
Project-based
CEA
Project-based
CEAscien
tific
research
Effects-based
CEAscien
tific
research
Effects-based
CEAscien
tificresearch
Key
strength
s
Limited
data
time
andco
strequirem
ents
Welle
stab
lish
edap
proach
Addresses
allo
rmostCEAgo
als
question
susingdirectmea
sures
ofsp
ecies
stressor
relation
ship
Can
incorporatemultipleda
tatype
san
dsources(qua
litativean
dqu
antitative
data)
Can
includeawiderange
ofstressors
Allow
squan
tification
ofeffects
(egloss
ofsu
itab
lehab
itat)w
hen
directsp
eciesndashstressor
resp
onse
orsp
eciesdataarelimited
Other
strengthslimitationsba
sedon
theex
tentto
whichqu
alitativeversus
quan
titative
dataareused
Key
limitations
Fa
ilsto
answ
erCEAquestion
sor
doe
sso
withuncertainreliab
ility
Inco
rporates
effectson
lydueto
hab
itat
loss
Highdata
timean
dco
strequirem
ents
Infreq
uen
tuse
interrestrialsystems
may
limitap
plication
Accuracy
andreliab
ilityhighly
dep
enden
ton
qualityan
dprecision
ofsp
ecies
hab
itat
associations
Restrictedto
speciesthat
can
beread
ilysampled
Fa
ilsto
address
man
yCEA
objectives
(interactionssh
ape
ofsp
eciesndashstressor
resp
onse)
Quan
titative
datarequirem
ents
Spec
ies
Nonemdash
Not
quan
titative
Lo
wmdash
Ifbased
onqualitative
species
hab
itat
associations
Veryhighmdash
Req
uires
species
metrics
across
fullgrad
ientof
stressors(andco
mbinationsif
testingforinteractions)
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareusedin
themod
el
Highmdash
Ifbased
onem
pirical
data
species
hab
itat
associations
(see
text)
Hab
itat
mdashsp
atiald
ata
Nonemdash
Not
quan
titative
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Stressormdash
spatiald
ata
Nonemdash
Not
quan
titative
Highto
very
highmdash
Tobe
effectiverequires
accu
rate
accessible
spatiald
ataon
ldquoindividualrdquostressorsoftenbroad
sector
catego
ries
(mining
oilg
as)
areused
Veryhighmdash
Tobeeffective
requires
accu
ratea
ccessible
spatiald
ataon
ldquoindividualrdquo
stressors
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Mahon and Pelech 207
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Tab
le4(continu
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
answ
erkey
CEAquestionsan
dap
plica
tionofCEA
What
isth
erelative
mag
nitudean
ddirection
ofindividual
stressor
effects
Xmdash
Only
based
onqualitativedata
Based
onrelative
contribution
ofstressor
toreducingam
ountof
estimated
suitab
lehab
itat
(req
uires
ldquobackfillingrdquo
individual
stressorsin
GISsee
text)
Pa
rameter
estimates
inmod
elrepresentmag
nitudedirection
and
certainty
ofstressor
effects
Stressor
effectstypically
are
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Relativeco
ntribution
ofstressors
toloss
ofsu
itab
lehab
itat
canbe
analysed
(req
uires
ldquoback-fillingrdquo
individual
stressorsin
GISsee
text)
What
isth
esh
apeofth
esp
eciesndashstressorresp
onse
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edNon
linea
rrelation
ships
(polyn
omial)canbetested
via
mod
elselectionan
dread
ily
grap
hed
Sh
apeof
theresp
onse
canbe
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Dostressors
combin
ead
ditivelyorin
tera
ctively
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edInteractionterm
scanbetested
forinclusion
viamod
elselection
procedures
Interactioneffectscanbeestimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rceof
inputmay
bequalitativeinform
ationor
empirical
mod
els)
Coe
fficien
tsgraphscanbeusedto
catego
rize
synergisticve
rsus
antago
nisticeffects
What
isth
eove
rallim
pac
tofcu
mulative
effectson
populations
Xmdash
Only
based
onqualitative
data
Based
onloss
ofsu
itab
lehab
itat
Sp
eciesndashhab
itatndashstressor
mod
els
canbeusedto
predictan
dco
mpare
speciesab
undan
cemea
sures
amon
gldquon
o-disturban
cereferen
cerdquo
curren
tan
dfuture
landscap
eco
nditionsbased
onGISla
ndscap
esimulation
Po
pulation
resp
onseoutputcanbe
compared
amon
gldquon
o-disturban
ce
referencerdquocu
rren
tan
dfuture
scen
arioswithmod
elinput(hab
itat
stressor
amou
nts)b
ased
onGIS
landscap
esimulation
Sp
eciesndashhab
itat
mod
elprediction
sforhab
itat
amou
nts
orsp
ecies
abunda
nce
canbe
compa
redam
ong
ldquono-disturban
cereferen
cerdquocu
rren
tan
dfuture
landscapecondition
sba
sedon
GISla
ndscapesimulation
What
isth
eove
rallim
pac
tonth
eco
mmunity
Xmdash
Only
based
onqualitative
data
Req
uires
calculatingspeciesndashhab
itat
mod
elsforallspeciescalculating
predicted
abunda
ncesin
reference
curren
tor
future
landscapesand
comparingcommunitymetrics
amon
gthesepredictedcommun
ities
Withsu
fficien
tdata
community
metrics
canbeco
mpared
amon
glowh
ighstressor
catego
ries
Req
uires
calculatingpop
ulation
resp
onsesforsp
eciesin
reference
curren
tor
future
landscap
esa
nd
comparingco
mmunitymetrics
amon
gthesepredictedco
mmunities
Com
munitymetrics
cansimilarly
becompa
redusingspeciesabun
dance
inreference
curren
tor
future
landscap
esa
spredictedfrom
empiricalspeciesndashhab
itatndashstressor
mod
els
How
certainar
eresu
lts
How
cance
rtainty
be
assessed
Verylow
mdashSu
bjectivenature
mak
esitdifficu
ltto
quan
tify
certainty
Verylowmdash
Ifspeciesndashhab
itat
basedon
qualitativeinform
ation(subjective
nature
mak
esitdifficu
ltto
quan
tify
certainty)
Highmdash
Statisticalsignificance
ofstressor
effectscanbeestimated
viaco
nfiden
ceintervalsof
param
eter
estimates
Moderateto
highmdash
Dep
endson
extent
towhichmod
elsareba
sedon
qualitativeversusem
piricalda
ta
Mod
eratemdash
Ifspecies
habitat
relation
shipsbasedon
empiricald
ata
Approachallowsuncertainty
ofrelation
shipsto
bequan
tified
and
inputinto
themod
els
Ran
geofstressors
evaluated
Highmdash
Not
limited
bysp
atial
dataon
stressors
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
andaccessible
spatiald
ata
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
and
accessible
spatiald
ata
Highmdash
Not
necessarily
limited
by
spatiald
ataon
stressorsifmod
els
based
onqualitativerelation
ships
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In more recent and complex approaches empirical speciesndashhabitat models are developed by sampling terrestrial speciesacross habitat types in a study area and using advanced statisticalanalyses to quantify species associations with different habitatsModels are termed resource selection functions (RSF) when spe-cies data (presenceabsence usedunused) are analyzed at smallwithin-population scales and species distribution models (SDM)or species abundance models (SAM) when data (occurrence abun-dance respectively) are analyzed at large range-level scales (Guisanand Zimmermann 2000 Guisan and Thuiller 2005 Elith andLeathwick 2009 Hegel et al 2010) For terrestrial birds current andemerging methods can be followed to convert species counts at asample station (the most common sample metric for terrestrialbirds) to a density estimate (Soacutelymos et al 2013 Matsuoka et al2014) Reporting species responses as densities (versus relativeabundance) provides (i) a relevant and readily communicatedmeasure of species response (ii) a standardized measure acrossregions and studies and (iii) an evaluation against conservationor management targets or population viability estimatesA range of statistical modeling frameworks are available to analyse
speciesndashhabitat relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) Typically habitat coefficients in these modelsare used to assign habitat rankings for species Both expert andempirically based habitat rankings require validation againstexternal data (or internal validations in the case of empiricalmodels) to ensure their reliability for an area (Pearce and Ferrier2000 Boyce et al 2002 Muir et al 2011) Once validated modelresults are used to assess the amount and distribution of suitablehabitat for species within a GIS (Millspaugh and Thompson 2009)Additionally when species responses have been converted to den-sity estimates coefficients can be coupled with GIS maps to esti-mate species densities or population sizes Within the context ofCEA the footprint or area of proposed projects existing projectsand disturbances and future land use scenarios can be overlaidon these maps to estimate the change in suitable habitat andassess effects on terrestrial species based on these habitat changeswithin the study area
322 Examples of applicationsWe highlight both project-based CEA and effects-based CEA
examples to demonstrate the flexibility of the habitat supplymethod Project-based CEAs typically use literature-based or hab-itat suitability models to identify map and quantify the area ofsuitable habitat for a given species within the study area We pro-vide a hypothetical example to explain how the habitat supplymethod is typically used to assess project-specific effects (Table 6)Changes in the amount of suitable habitat during the project(application phase eg from 10 to 6 of study area in Table 6)and after the project (closureremediation phase eg 0 changein Table 6) are estimated relative to current or baseline The hab-itat supply method is not typically used to assess CE because theextent to which existing or past stressors have affected pre-existinghabitat types is often not known Instead CE requirements areoften met by comparing the total area of the proposed project (eg5 of study area in Table 6) to the total area of other stressors regard-less of habitat affected (eg 35 Table 6) Thus in high or moder-ately disturbed areas these comparisons may suggest that a projectwould have minimal added effects relative to existing stressorsWhere data and resources allow some project-based CEAs mayuse historic maps or create hypothetical no-disturbance land-scapes within a GIS (eg see methods in ABMI 2017 Mahon et al2019) to assess how other stressors may have affected specific habi-tats However resource requirements (time data technical exper-tise) place this type of assessment beyond themandate or capacity ofmost project-based CEAs We include an example of the habitat sup-ply method from a recent project-based CEA from the IAAC RegistryT
able
4(conclud
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
inco
rpora
tetrad
itional
knowledge
Yes
Notusu
ally
Notusu
ally
Yes
Statistica
ltech
nical
expertise
required
Low
tomoderatemdash
Mod
eratewith
form
almod
eling
Moderate
High
Highto
very
high(specialized)
Tim
efra
me
Month
s
Month
sto
ltyea
rmdash
For
speciesndashhab
itat
mod
elsbased
onqualitativedata(dep
enden
ton
statusof
spatiald
ata
detailo
fthe
mod
ela
vailab
ilityof
species
inform
ation)
gtYea
rto
multi-ye
ar
Speciesdataco
llection
may
nee
dto
occu
rov
ergt1ye
arto
provide
sufficien
tsamples
Month
sto
gtmulti-ye
armdash
Highly
dep
enden
ton
sourceof
datainput
(existingdata
new
empirical
data
collection
)
Statisticalm
odel
dev
elop
men
tan
drefinem
entcanbetime
consu
ming
Modelingap
proachmay
require
additional
wee
ksor
month
s
gtYea
rmdash
Forem
piricalspe
ciesndashh
abitat
mod
elsthat
requ
irenew
data
collection
Dep
enden
ton
timerequired
toco
mpile
evaluatean
dcatego
rize
hab
itat
andstressor
data
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Tab
le5
Exa
mple
ofaqualitativereview
(matrixtable)for
aproject-based
CEA
Valued
compon
ent
Potential
effects
Mitigation
compen
sation
Residual
effect
Sign
ifican
ceof
residual
effects
Eco
logical
societal
value
Ove
rallsign
ifican
ceMag
nitude
Spatiale
xten
t
Freq
uen
cy
probab
ility
Duration
Rev
ersibility
Graywolf
Assoc
iatedwith
major
preysp
ecies
(moo
sed
eer)A
t
closure
increa
sed
area
sof
grasslan
d
willc
reatewhite-
tailed
dee
rhab
itat
andpoten
tially
higher
dee
ran
d
wolfnumbers
Limitfootprintof
disturbed
area
s
Increase
indeeran
d
wolfp
opulation
s
over
timeResidual
adverseeffect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
highqualitymoo
se
hab
itattherefore
limited
wolf
pop
ulation
MIN
IMAL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
is
long-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
is
reversibledu
ring
site
reclam
ation
Moo
sefollow
edby
wolvessh
ould
return
tosite
at
curren
tlevels
LOW
Wolvesarecommon
inthelocaland
region
alstudy
areasLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
wolve
s
aremob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
American
black
bea
r
Initialm
igration
duringsite
preparationan
d
constru
ction
return
tosite
area
afterperiodof
human
noise
hab
ituation
Limitfootprintof
disturbed
areas
Establishwildlife
policyto
minim
ize
human
interaction
withwildlifean
d
decrease
poten
tial
forhab
ituation
Increa
sedseason
al
use
ofthesite
over
timedep
endson
succession
of
reclaimed
area
s
Residual
adve
rse
effect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
abov
eav
erag
ebea
r
hab
itatM
INIM
AL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
islong-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
reversibledu
ring
site
reclam
ation
New
grasslan
d
hab
itat
may
increase
the
season
alhab
itat
use
bybe
ars
LOW
Bearde
nsity
inthe
study
site
islikely
comparab
leto
that
inthesurrou
nding
landscapeLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
bea
rsare
mob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
NoteT
hetable
summarizes
theproposed
project
effectsmitigationan
dco
mpen
sation
mea
surespoten
tial
residual
effects(theen
vironmen
tale
ffectof
aproject
that
remainsaftermitigationmea
sureshav
ebee
nim
plemen
ted)sign
ificance
ofresidual
effectsin
considerationof
ava
rietyof
assessmen
tfactors(ratings
inboldface
type)a
ndwhether
asign
ificantad
verseen
vironmen
tale
ffecton
ava
lued
compon
entisex
pected
asaresu
ltof
project
implemen
tation
Sou
rceEnvironmen
talImpactStatem
entfortheMarathon
PGM-CuProject20
12Thetable
represents
description
san
dratings
summarizingtheen
vironmen
tale
ffects
ofametal
minedev
elop
men
ton
twova
lued
compon
ents
(graywolfan
dAmerican
black
bea
r)
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(Environmental Impact Statement for the Springbank Off-streamReservoir Project 2018) to estimate area changes in the amountof suitable habitat (based on habitat suitability models) for selectVCs Spraguersquos Pipit (Anthus spragueii) and Northern Leopard Frog(Lithobates pipiens) during different flood scenarios created by waterdiversion to a channel and reservoir within the proposed projectarea (Table 7)Effects-based CEAs typically involve the creation and application
of empirical speciesndashhabitat models within the study area Maceet al (1999) evaluated the CE of human and resource activity ongrizzly bear (Ursus arctos) habitat inwesternMontana during springsummer and fall seasons The authors modelled bear resourceselection from telemetry data satellite imagery elevation humanactivity points roads and trails and extrapolatedmodels to a largerregion with similar climate and vegetation Quantifying and map-ping areas with a high probability of use by bears in each seasonwas used to inform conservation and management actions (egroad or trail closures within important seasonal habitats habitatrestoration future road creation) Roumldder et al (2016) outline aninnovative approach to integrate SDM and potential connectivitymodels to identify connected and isolated populations and habitatcorridors for a species of conservation concern the Sand Lizard(Lacerta agilis) in western Germany (Fig 2) Quantifying potentialconnectivity areas (for movement like dispersal or migration) wasused to identify areas of strong connectivity persistent isolationand stable connective networks needed to connect populationsThis proposed framework may be particularly useful for proposeddevelopments that affect (i) portions of a previously larger inter-connected population or (ii) connective features between perma-nently colonized habitat patches Additional examples from thepublished literature are listed in Table 3
323 Advantages and limitations CEA questions and whenwhyto use3231 AdvantagesAdvantages of the habitat supply method include its ability to
quantify effects using loss of suitable habitat This approach
which has wide-spread application in wildlife research may besuitable when (i) data to map and measure habitat change areavailable but empirical data on species response to specific stres-sors are limited (ii) collaborative approaches focused on localissues and values are important (eg speciesndashhabitat associa-tions may be based on both qualitative (TK local and expertknowledge) and quantitative (speciesndashhabitat models) data) and(iii) data to develop empirical speciesndashhabitat models and adjusteddensity estimates are available so that total impacts can be com-pared to current and future population targets (eg Mahon et al2014)
3232 LimitationsLimitations to the habitat supply modelling approach include
the availability of accurate publicly available spatial data forhabitats and stressors and reliable specieshabitat associationsfrom the region of interest If additional data are required torefine existing habitat suitability models or create new empiricalspeciesndashhabitat models within a study area stakeholders or part-ners should a priori develop rigorous sampling designs and sur-vey protocols to meet project objectives We note two additionalcautions when using this approach Specieshabitat associationsbased on limited empirical data from within the region or datafrom outside the region require model validation and testing(Muir et al 2011) Finally stressor effects are limited to how stres-sors contribute to the loss of suitable habitat (habitat changesare used to infer population consequences) Habitat-based analy-sis alone may not be useful because (i) habitat is assumed to bea useful measure of population status (ii) habitat needs of the tar-get species or VC are assumed to be known (Schultz 2010) and(iii) specific stressor effects on the target species or VC are notquantified (eg the shape magnitude and direction of speciesrsquoresponse to stressors indirect or novel effects of stressors on speciesor interactions among stressors) Failure to identify which stressorsare influencing target species or VCsmay limit future research intobiological mechanisms (eg habitat selection movement disper-sal reproduction survival predatorndashprey dynamics) needed to
Fig 1 Example of a qualitative review (conceptual modeldiagram) for an effects-based CEA Figure represents an annual life cycle ofevents of Greater Sage-Grouse responding to oil and gas development in southwest Wyoming USA (from Heinrichs et al 2019 reproducedwith permission of Ecol Appl Vol 29 copy2019 Ecological Society of America)
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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
vegetation layer including ldquobackfilledrdquo vegetation in human footprints(Version 6) Alberta Biodiversity Monitoring Institute Edmonton Reportavailable from httpsabmicahomehtml [last accessed 13 November 2018]
Austin M 2007 Species distribution models and ecological theory a criticalassessment and some possible new approaches Ecol Modell 200 1ndash19 doi101016jecolmodel200607005
Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
BC Resource Inventory Committee 1999 British Columbia Wildlife HabitatRating Standards Available from httpswww2govbccaassetsgovenvironmentnatural-resource-stewardshipnr-laws-policyriscwhrspdf [last accessed13 November 2018]
Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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iron
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IFIC
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NA
TL
LA
B o
n 12
07
21Fo
r pe
rson
al u
se o
nly
Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
Canadian Environmental Assessment Agency (CEAA) 2015 Assessing Cumula-tive Environmental Effects under the Canadian Environmental AssessmentAct 2012 March 2015 Report Available from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
Canadian Environmental Assessment Agency (CEAA) Expert Panel Review 2017Building common ground a new vision for impact assessment in Canada Thefinal report of the expert panel for the review of Environmental assessmentprocesses Available from httpswwwcanadacaenservicesenvironmentconservationassessmentsenvironmental-reviewsenvironmental-assessment-processesbuilding-common-groundhtml [last accessed 10 April 2020]
Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
Environmental Impact Statement for the Springbank Off-stream ReservoirProject 2018 Environmental Impact Assessment of the Springbank Off-stream Reservoir Project Stantec Incorporated Available from httpsiaac-aeicgcca [last accessed 15 June 2020]
Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
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Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
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Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
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Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
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Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
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Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
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Mahon and Pelech 223
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Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
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Swor T and Canter L 2011 Promoting environmental sustainability via anexpert elicitation process Environ Impact Assess Rev 31 506ndash514 doi101016jeiar201101014
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US Fish and Wildlife Service (USFWS) 1980 Habitat as a basis for environ-mental assessment 101 ESM US Department of the Interior Fish andWildlife Service Washington DC
US Fish and Wildlife Service (USFWS) 1981 Standards for development ofhabitat suitability models 103 ESM US Department of the Interior Fishand Wildlife Service Washington DC
Usher PJ 2000 Traditional ecological knowledge in environmental assess-ment and management Arctic 53 183ndash193 doi1014430arctic849
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Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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28 August 2019 follow technical guidance associated with theCanadian Environmental Assessment Act 2012 (CEAA 2014 2018)while those initiated after 28 August 2019 follow technical guid-ance associated with the Impact Assessment Act 2019 (IAAC 2019)Critical to the CEAA technical guidance (CEAA 2014 2018) andoperational policy statement (CEAA 2015) is identifying meas-ures that would mitigate adverse CE (step 3) and determiningthe significance of any adverse CE taking into account theimplementation of mitigation measures (step 4) Similarly newIAAC technical guidance (see httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancesection-22-factors-considered-descriptionshtml) requires the consideration of anyCE (section 22 (a) (ii)) and any measures that would mitigate anyadverse effects of the designated project (section 22 (a) (b)) Project-based CEAs within northern jurisdictions (Yukon TerritoryNorthwest Territories Nunavut) follow specific technical guidance(Table 1) The focus of most project-based CEAs in Canada is toidentify and mitigate the individual stressors associated with theproposed project This process involves determining whetherproject activities in the context of other past present or futureactivities will cause significant adverse CE after the implementationof proposedmitigationmeasuresEffects-based CEAs conducted as part of Regional Assessments
under the IAAC are studies conducted in areas of existing proj-ects or anticipated projects within provincial jurisdictions toinform planning and management of CE and inform projectimpact assessments The Minister of Environment and ClimateChange appoints a committee or asks the IAAC to conduct a Re-gional Assessment establishes the terms of reference for eachRegional Assessment and reviews the final Regional Assessmentreport Regional Assessments may differ in their goals activitiesand approaches that can include filling data gaps and analyzing
trends establishing thresholds and standard mitigation or iden-tifying regional development objectives and scenarios (httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidanceregional-assessment-impact-assessment-acthtml) We notethat Regional Assessments under the IAAC are a new process(two Regional Assessments have been initiated and only oneRegional Assessment has been completed as of November 2020)Specific guidance and direction would ensure that future RegionalAssessments meet objectives of planning assessing and managingCE Regional Assessments under the IAAC have the potential to bea valuable and useful process for assessing CE in a region usinginnovative methods approaches and tools We promote the useof rigorous CEA analytical methods within Regional Assessments to(i) quantify current and future impacts of human activities andclimate change (ii) manage where when and how human activityoccurs and (iii) produce alternate scenarios to balance humanactivity and species conservationEffects-based CEAs outside of regulatory processes can be con-
ducted in any jurisdiction in Canada by interested partnersgroups or agencies (eg academic institutions provincial or ter-ritorial government agencies environmental nongovernmentalagencies Indigenous communities or groups research groupsland use planning commissions) The focus of most nonregula-tory effects-based CEAs in Canada is often on assessing and ana-lyzing stressor-effect relationships and simulating land andresource development scenarios within a specific region of inter-est This typically involves quantifying regional effects of stres-sors often associated with multiple sectors identifying types ofstressor combinations measuring uncertainty and (or) simulat-ing the impacts of stressors on habitats and terrestrial speciesinto the future using various landscape climate resource ormanagement scenarios
Table 1 Types of cumulative effects assessment (CEA) within Canada
Type of CEA Jurisdiction Agency Guidance CEAmethodsapproach
Project-based CEA1 CEA within EAIA Provinces CEAA Examine cumulative effects (CE) of a project on VCs usingfive steps (1) scoping
IAAC Examine CE (changes to environment health social and economic conditions)usingfive steps preceding (interim guidance httpswwwcanadacaenimpact-assessment-agency CEAA 2014 2018)
YT YESAB Examine CE onVCs by considering project effects (magnitude duration timinglikelihood reversibility and spatial extent) and other past current or futureproject effects (httpswwwyesabca YESAB 2019)
NT MVEIRB Examine CE using four steps (1) identifying parts of the environment affectedby project (2) determining parts of the environment affected by other pastpresent or future projects (3) predicting effects of proposed project andother projects (4) identifying ways tomanage combined impacts (httpsreviewboardca MVEIRB 2004)
NU NIRB Identify and document cumulative impacts from the project and any otherpast current or future project and determine whether project is of regionalinterest (httpswwwnirbca NIRB 2020)
Effects-based CEA1 Regional Assessment Provinces IAAC Examine CE using methods identified by the appointed committee or the
IAAC and documented in the terms of reference2 Regional Projects Any location or
Examine CEwithin region of interest using questions andmethods defined bypartners
Note Jurisdiction definitions mdash YT Yukon Territory NT Northwest Territories NU Nunavut Agency definitions mdash CEAA Canadian Environmental AssessmentAgency IACC Impact Assessment Agency of Canada administers projects under Canadian Environmental Assessment Act 2012 and Impact Assessment Act 2019 YESABYukon Environmental and Socio-economic Assessment Board administers projects under Yukon Environmental and Socio-economic Assessment Act 2003 MVEIRBMackenzie Valley Environmental Impact Review Board administers projects underMackenzie Valley Resource Management Act 1998 NIRB Nunavut Impact Review Boardadministers projects under Nunavut Planning and Project Assessment Act 2013 partners can be academic institutions provincial or territorial government agenciesenvironmental non-governmental agencies Indigenous communities or groups research groups land use planning commissions Acronyms EA environmentalassessment IA impact assessment VC valued components
Mahon and Pelech 203
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There are multiple CEA definitions frameworks (see Duinkeret al 2013) processes and questions (as described previously andin Table 1) In this review we define key science-based CEA objec-tives relevant to both project-based and effects-based CEAs as thefollowing (i) describe or characterize overall CE (ii) determinethe rank or magnitude and direction (eg positive or negative) ofindividual stressor effects (iii) describe the shape of the relation-ship between stressors and species (eg linear or nonlinear)(iv) determine how stressors combine to affect species (eg addi-tive interactivendashsynergistic interactivendashantagonistic compensa-tory or masking) (v) relate CE to baseline or pre-disturbanceconditions defined thresholds or management triggers and(vi) describe or assign certainty or probability of occurrence ofpredicted effects Within our review we assess the capacity ofCEA methods for species in terrestrial systems to address thesescience-based CEA objectives
3 Description and evaluation of CEAmethodsHere we provide an overview of current methods for project-
based and effects-based CEAs that have or could be used for terres-trial species We conducted a three-stage review to (i) identify CEAmethods used in project-based CEAs and effects-based CEAs and(ii) document relevant examples First we reviewed all active assess-ments (as of 15 June 2020) listed on the Canadian Impact AssessmentRegistry (httpsiaacgcca) including Environmental Assess-ments under Canadian Environmental Assessment Act 2012 (n = 50)Environmental Assessments by Review Panel under CanadianEnvironmental Assessment Act 2012 (n = 8) Impact Assessmentsunder Impact Assessment Act 2019 (n = 2) and Regional Assessmentsunder Impact Assessment Act 2019 (n = 2) and recorded the types ofmethods used within the CEA (eg Environmental ImpactStatement Impact Statement Regional Assessment) in Table 2Second we searched for published reviews of CEA methods
within the scientific literature and the CEA guidance literature(Table 2) We collapsed similar approaches identified in stages 1and 2 into four broad categories of CEA methods qualitativereview habitat supply models empirical speciesndashstressor modelsand decision support models (focusing on Bayesian belief network(BBN) models) We distinguished data products like indicators (iea measurable variable chosen to represent the state of a compo-nent) and indices (ie composite numerical value for some envi-ronmental attribute usually comprised of two or more variables)that can be used within a CEA process from analytical CEAmethods We also distinguished data tools like geographic in-formation systems (GIS) used to produce spatial data productsincluded within most modern project-based or effects-basedCEAs from analytical CEAmethodsThird we conducted targeted searches using Web of Science
across the four CEA methods to find published examples for spe-cies in terrestrial systems We include some CEA publishedpapers as examples in the description of each CEAmethod in sub-sequent sections and list all papers reviewed by CEA methodincluding study area location and target terrestrial species orgroup (Table 3) CEA methods fall on a gradient of increasingcomplexity data requirements and technical expertise In manycases a combination of CEA methods will be required to effec-tively answer science-based CEA questions Our intent is not anexhaustive review of all CEA methods or an intensive review ofone category of methods Instead we describe each CEA methodand provide relevant examples We also identify advantages andlimitations explain how methods address science-based CEAquestions (Table 4) and provide direction on when and why touse specific CEAmethods
31 Qualitative review
311 Description of methodThis broad category of methods includes a variety of approaches
to collect (questionnaires interviews workshops literature reviews)
and summarize (checklists matrices qualitative or conceptualmodelsdiagrams) qualitative data or information (eg expertopinion published or unpublished information) as an exclusiveor primarymeans to assess CE (Cocklin et al 1992 Smit and Spaling1995 Duinker et al 2013 CEAA 2014 2018 see Tables 2 and 3)rather than direct measurements of species response to habitatchange or stressors This approach would typically include assess-ments with high constraints on data availability (for species habi-tats and stressors) time and (or) cost For example informationon stressors may be based on qualitative descriptions or approxi-mations of development (eg hard copies of maps written orverbal descriptions of proposed developments or stressors) ratherthan spatial and attribute data on stressors within a GIS In somecases qualitative datainformation is ranked to provide a categori-cal assessment of effects (eg using literature or expert review torankweight potential effects of stressors on species) Formal con-sensus techniques and qualitative modelling can also be used (egDelphi method see Linstone and Turoff 2002 fuzzy cognitivemapping see Gray et al 2015) although these remain infrequentfor most assessments of terrestrial species Although also a formof qualitative review we consider independent traditional knowl-edge (TK) studies or combined TK and scientific knowledge (SK) stud-ies (eg co-production of knowledge) to be separate from thiscategory due to distinct methods used to select experts with dif-ferent types of knowledge (eg local harvesters and huntersElders government employees scientists) and conduct the elici-tation procedure (Huntington 1998 Usher 2000 Polfus et al 2014Beacutelisle et al 2018)Qualitative review is common among project-based CEAs for
terrestrial species (see Table 2 stage 1) where proponents typi-cally conduct literature reviews to collect information and usechecklists and matrices to rank potential effects on VC (eg lowto high) Qualitative review is less common among effects-basedCEAs for terrestrial species (outside of TK studies) with the excep-tion of (i) potential stressor effects that are difficult to map orquantify (eg pesticide use pollution species interactions) and(ii) complex systems where multiple stressor effects are difficultto visualize or comprehend In the latter case conceptual modelsdiagrams can be combined with other quantitative CEA approaches(eg habitat supply empirical speciesndashstressor and decision supportmodels) where the conceptual model is shared refined and usedto guide monitoring and research questions and hypothesesamong partners (Marcot et al 2006 Lindenmayer and Likens 2010Nelitz et al 2015)
312 Examples of applicationsWe highlight two examples of qualitative review methods and
list additional examples from the published literature in Table 3The first example from a recent project-based CEA from theIAAC Registry (Environmental Impact Statement for the MarathonPGM-Cu Project 2012) uses a matrix table (Table 5) to identify theVCs potential effects mitigation measures and residual effectsof the project For each VC gray wolf (Canis lupus) and Americanblack bear (Ursus americanus) a rank or level is assigned to eachparameter used to characterize residual effects (magnitude spa-tial extent frequency duration reversibility ecological or soci-etal value) These levels are then combined to create a measureof overall significance a required first step in conducting theCEA (CE are defined as the environmental effects resulting fromthe project in combination with other projects or activities) Thesecond example from an effects-based CEA uses a conceptualmodeldiagram to illustrate the Greater Sage-Grouse (Centrocercusurophasianus) annual life cycle including influence of oil and gasdevelopment on site fidelity dispersal and demography (Fig 1)The life cycle informs the population simulation model used totrack population dynamics and habitat use of simulated Sage-Grousethrough space and time as they respond to changing landscape
204 Environ Rev Vol 29 2021
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conditions (climate-induced vegetation change) and exposure tooil and gas stressors (Heinrichs et al 2019)
313 Advantages and limitations CEA questions and whenwhyto use
3131 AdvantagesThe advantage of qualitative review is its utility when quantita-
tive data on habitat and stressors and time and (or) financialresources are limited The approach also has the ability to (i) offera collaborative flexible and accessible approach when localissues and values are important (eg TK or combined TK and SKstudies) (ii) identify a wide range of stressors and mechanisms
(eg indirect effects species interactions stressors that cannotbe effectively mapped) and (iii) serve as a conceptual frameworkwithin a CEA using one or more quantitative methods to assessand (or) simulate multiple stressor effects (eg empirical speciesndashstressor models Bayesian belief network (BBN) models simulationmodels)
3132 LimitationsThe limitation of qualitative review is uncertainty surrounding
the objectivity and reliability of assessments particularly whenexpert opinion is applied to novel locations or stressors Specifi-cally these limitations include the inability to quantify any stres-sor related impacts including effects on habitat area (eg amount
Table 2 Cumulative effects assessment (CEA) methods identified in the stage one review (Canadian Impact Assessment Registry) and the stagetwo review (published reviews of CEAmethods and publicly available CEA guidance documents)
Stage CEAmethods
Stage 1 Canadian Impact Assessment Registry Total 2262 (35) with Environmental Impact Statement ImpactStatement Regional Assessment Report
Environmental Assessment under Canadian Environmental Assessment Act2012 (n = 50)
1650 (32) with Environmental Impact Statement1516 used QR416 used QR and HSM
Environmental Assessment by Review Panel under CanadianEnvironmental Assessment Act 2012 (n = 8)
58 (625) with Environmental Impact Statement55 used QR35 used QR and HSM15 used QR HSM and SM
Impact Assessment under Impact Assessment Act 2019 (n = 2) 02 (0) with Impact StatementRegional Assessment under Impact Assessment Act 2019 (n = 2) 12 (50) with Regional Assessment Report
1 used QR HSM and DSM
Stage 2 Reviews of CEAmethodsCocklin et al 1992 Environmental checklists matrix approaches inputndashoutputmethods
approaches including network analysisCanter and Atkinson 2011 Indicators and indices habitat suitability modelsDuinker et al 2013 GIS scenarios thresholds indicators and indices network analysis
matrices habitat suitability modelshabitat selection modelshabitat supply models stressor-based and effects-based approachesmultivariate statistical analysis air pollutionmodels landscapemodels animal population simulation models water-basedsimulationmodels public engagement community-basedmonitoring population viability analysis costndashbenefit analysisvisual amenity analysis traffic-light decision support systemsustainability appraisal
Hodgson and Halpern 2019 Experiment meta-analysis single species models mappingqualitative models multi-species models (aquaticmarine)
Marcot et al 2001 Marcot et al 2006 McCann et al 2006 Uusitalo 2007 Bayesian networkmodelsBayesian belief network (BBN) models
Stage 2 CEA guidance documentsTechnical Guidance for Assessing CEA under Canadian EnvironmentalAssessment Act 2012 Draft (CEAA 2014) Technical Guidance forAssessing CEA under Canadian Environmental Assessment Act 2012InterimGuidance (CEAA 2018)
Questionnaires and interviews checklists and matrices network andsystems analysisdiagrams indicators and indices conceptual andnumerical models trend analysis spatial analysis
Section 22-Factors to be considered (IAAC 2019 httpswwwcanadacaenimpact-assessment-agency)
New IAAC guidance in preparation interim guidance is CEAA (2018)
Consideration of cumulative effects in YESAB Assessments (YESAB 20052019)
No CEAmethods
Environmental Assessment Guidelines (MVEIRB 2004) No CEAmethodsProponents Guide NIRB Technical Guide Series (NIRB 2020) No CEAmethods
Note Agency definitions CEAA Canadian Environmental Assessment Agency IACC Impact Assessment Agency of Canada administers projects under CanadianEnvironmental Assessment Act 2012 and Impact Assessment Act 2019 YESAB Yukon Environmental and Socio-economic Assessment Board administers projects underYukon Environmental and Socio-economic Assessment Act 2003 MVEIRB Mackenzie Valley Environmental Impact Review Board administers projects underMackenzie ValleyResource Management Act 1998 Nunavut Impact Review Board (NIRB) administers projects under Nunavut Planning and Project Assessment Act 2013 CEA methoddefinitions QR qualitative review HSM habitat supply models SM simulation models DSM decision support models
Mahon and Pelech 205
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of suitable habitat lost) and direct (eg habitat loss mortality) orindirect stressor effects on species (eg altered habitat selectionreduced demographic rates like reproduction or survival)
3133 Science-based CEA questionsSee Table 4 to identify how qualitative review methods address
key science-based CEA questions
3134 Whenwhy to usemdash project-based CEAsldquoWhen to userdquo when quantitative data on species and spatial
data on habitats or stressors limit the development of speciesndashhabitatmodels (to assess change in habitat) and (or) speciesndashhabitatndashstressor models (to assess species response to habitat and stressors)Qualitative data and qualitative reviewmethods can be used to iden-tify rank and categorize stressor effects ldquoWhy to userdquo allows for acomprehensive listing and ranking of potential stressor effects onVCs in relation to (i) mitigation measures for the proposed projectand (ii) other past current or future projects
3135 Whenwhy to usemdash effects-based CEAsldquoWhen to userdquo when systems are complex conceptual diagrams
can be combined with other quantitative methods and used to(i) identify multiple stressors and (or) pathways of effects on dif-ferent life stages of target species or VCs (see Fisher and Burton2018 Pirotta et al 2018 and Heinrichs et al 2019 for examples)and (ii) outline the analysis process or framework (see Munns2006 and Katzner et al 2020 for examples) ldquoWhy to userdquo allowsfor a visual representation of the system (stressors ecologicaldrivers individual and population responses) and the analysisprocess
32 Habitat supply models
321 Description of methodIn this category we include various forms of speciesndashhabitat
modelling currently the most common approach to terrestrialspecies assessment in most project-based CEAs as well as in con-servation planning and landscape management (eg Morrisonet al 2006) Approaches vary in complexity from simple literature-based habitat rankings to complex statistical models of empirical
speciesndashhabitat relationships (see Table 3) A defining characteristicof this method for CEA is that habitat is used as a surrogate for theidentified terrestrial species (or as a VC itself) For habitat supplymodels quantitative changes in the amount and (or) quality of aspeciesrsquo habitat (typically categorized and measured within a GIS)are used as the primary measure of CE In contrast for empiricalspeciesndashstressormodels the species direct response to one ormorestressors is used as the primary measure of CE We provide morescientific background to this approach subsequently however ingeneral this approachwith respect to CEA typically involves
1 Developing speciesndashhabitat associations or models Thisrequires ranking the suitabilityeffectivenessfunctionality ofmeasurable habitat categories for target species using expertopinion literature review and (or) empirical speciesndashhabitatcorrelations
2 Quantifying changes in the amount of this habitat as a resultof the proposed project or projects and existingfuture stressors
3 Inferring effects on species as a result of these habitat changes
Speciesndashhabitat models can be developed using a variety ofexpert opinion and statistical modelling approaches have a longhistory in wildlife management and landscape planning and arewell-documented in the literature (eg Morrison et al 2006Millspaugh and Thompson 2009 Guisan et al 2013) Standardsfor developing literature and expert opinion based habitat suit-ability indices (HSI) date back to the 1980s (US Fish and WildlifeService (USFWS) 1980 1981) and many jurisdictions have simi-larly developed protocols for wildlife habitat rankings in Canada(BC Resource Inventory Committee 1999 Clarke 2012) Thesehabitat suitability models typically use experts and (or) compilespecies information from existing literature to rank habitats fora species (eg on a 0ndash1 scale or similar categorical ranking sys-tem) Additional variables that may modify habitat quality (egproximity to roads) are also often considered to differentiatebetween habitat capability (the potential of the habitat to sup-port the species under optimal conditions) and habitat suitabilityor effectiveness (the actual ability of the habitat to support the spe-cies in a specific study area)
Table 3 Cumulative effects assessment (CEA) methods identified in the stage three review (targeted reviews of CEA methods) qualitativereview habitat supply models empirical speciesndashstressor models and decision support models (Bayesian belief network (BBN) models)
Stage 3 Targeted reviews of CEAmethods References
Qualitative review Chilima et al 2013 (Ontario CAN watersheds) Cooper 2011 (United Kingdom multiple species)Swor and Canter 2011 (Pennsylvania USA water fish mussels riparianfloodplain resources)
Habitat supply models Canter and Atkinson 2011 (USA multiple species) Houle et al 2010 (Quebec CAN gray wolf)Johnson et al 2005 (Northwest Territories CAN caribou gray wolf grizzly bear wolverine) Maceet al 1999 (Montana USA grizzly bear) Roumldder et al 2016 (Cologne Germany Sand Lizard)
Empirical species-stressor models Sawyer et al 2009 (Wyoming USA mule deer) Houle et al 2010 (Quebec CAN gray wolf) Salice et al2011 (South Carolina USA Eastern Narrow-mouthed Toad) Salice 2012 (southeast USA EasternNarrow-mouthed Toad) Wilson et al 2013 (northwest Alaska USA caribou) Goodale andMilman2019 (offshore regions waterfowl waterbirds landbirds) Hodgson et al 2017 (various locationsinsects birds fish ungulates) Jones et al 2017 (Muskoka RiverWatershed CAN benthic invertebrates)Fisher and Burton 2018 (northeast Alberta CAN gray wolf white-tailed deermoose American blackbear coyote Canada lynx fisher red fox snowshoe hare American red squirrel) Claireau et al 2019(western France 10 bat species) Daniel and Koper 2019 (southern Alberta CAN 5 grassland landbirdspecies) Heinrichs et al 2019 (Wyoming USA Greater Sage-Grouse) Mahon et al 2019 (northeastAlberta CAN 27 boreal landbird species) Katzner et al 2020 (California USA roadrunner hawk)Leston et al 2020 (northeast Alberta CAN 20 boreal landbird species)
McNay et al 2006 (north-central British Columbia CAN caribou) Steventon et al 2006 (north coastof British Columbia CAN Marbled Murrelet) Howes et al 2010 (eastern Australia woodlandlandbirds) MacCracken et al 2013 (Bering and Chukchi Seas Pacific walrus) Ban et al 2014 (GreatBarrier Reef Australia reef coral) Fortin et al 2016 (Alaska USA brown bears) Mantyka-Pringleet al 2016 (South East Queensland Australia macroinvertebrates fish) Mantyka-Pringle et al 2017(Slave River Delta Northwest Territories CAN fish waterfowl mammals)
206 Environ Rev Vol 29 2021
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r pe
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Tab
le4
Summaryco
mparison
ofan
alytical
method
sforcu
mulative
effectsassessmen
t(CEA)for
terrestrialspecies
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Cumulative
effectsof
stressors
bas
edon
Exp
ertknow
ledge
literature
review
oflikelyeffects
Estim
ated
chan
gein
amou
ntof
suitab
lehab
itat
forsp
eciesas
aresu
ltof
stressorssu
itab
lehab
itat
isch
arac
terize
dusingex
pert
opinionliterature
and(or)
empirical
data
Directmea
suremen
tsof
speciesrsquo
resp
onse
tograd
ientof
stressors
andco
mbinationsof
stressors
Mod
eled
respon
seof
speciesab
unda
nce
orhab
itat
tocombined
hab
itatstressor
andother
inpu
ts(in
putsmay
bequ
alitativeor
empiricald
ata)
Most
commonly
asso
ciated
with
Project-based
CEA
Project-based
CEAscien
tific
research
Effects-based
CEAscien
tific
research
Effects-based
CEAscien
tificresearch
Key
strength
s
Limited
data
time
andco
strequirem
ents
Welle
stab
lish
edap
proach
Addresses
allo
rmostCEAgo
als
question
susingdirectmea
sures
ofsp
ecies
stressor
relation
ship
Can
incorporatemultipleda
tatype
san
dsources(qua
litativean
dqu
antitative
data)
Can
includeawiderange
ofstressors
Allow
squan
tification
ofeffects
(egloss
ofsu
itab
lehab
itat)w
hen
directsp
eciesndashstressor
resp
onse
orsp
eciesdataarelimited
Other
strengthslimitationsba
sedon
theex
tentto
whichqu
alitativeversus
quan
titative
dataareused
Key
limitations
Fa
ilsto
answ
erCEAquestion
sor
doe
sso
withuncertainreliab
ility
Inco
rporates
effectson
lydueto
hab
itat
loss
Highdata
timean
dco
strequirem
ents
Infreq
uen
tuse
interrestrialsystems
may
limitap
plication
Accuracy
andreliab
ilityhighly
dep
enden
ton
qualityan
dprecision
ofsp
ecies
hab
itat
associations
Restrictedto
speciesthat
can
beread
ilysampled
Fa
ilsto
address
man
yCEA
objectives
(interactionssh
ape
ofsp
eciesndashstressor
resp
onse)
Quan
titative
datarequirem
ents
Spec
ies
Nonemdash
Not
quan
titative
Lo
wmdash
Ifbased
onqualitative
species
hab
itat
associations
Veryhighmdash
Req
uires
species
metrics
across
fullgrad
ientof
stressors(andco
mbinationsif
testingforinteractions)
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareusedin
themod
el
Highmdash
Ifbased
onem
pirical
data
species
hab
itat
associations
(see
text)
Hab
itat
mdashsp
atiald
ata
Nonemdash
Not
quan
titative
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Stressormdash
spatiald
ata
Nonemdash
Not
quan
titative
Highto
very
highmdash
Tobe
effectiverequires
accu
rate
accessible
spatiald
ataon
ldquoindividualrdquostressorsoftenbroad
sector
catego
ries
(mining
oilg
as)
areused
Veryhighmdash
Tobeeffective
requires
accu
ratea
ccessible
spatiald
ataon
ldquoindividualrdquo
stressors
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Mahon and Pelech 207
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Tab
le4(continu
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
answ
erkey
CEAquestionsan
dap
plica
tionofCEA
What
isth
erelative
mag
nitudean
ddirection
ofindividual
stressor
effects
Xmdash
Only
based
onqualitativedata
Based
onrelative
contribution
ofstressor
toreducingam
ountof
estimated
suitab
lehab
itat
(req
uires
ldquobackfillingrdquo
individual
stressorsin
GISsee
text)
Pa
rameter
estimates
inmod
elrepresentmag
nitudedirection
and
certainty
ofstressor
effects
Stressor
effectstypically
are
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Relativeco
ntribution
ofstressors
toloss
ofsu
itab
lehab
itat
canbe
analysed
(req
uires
ldquoback-fillingrdquo
individual
stressorsin
GISsee
text)
What
isth
esh
apeofth
esp
eciesndashstressorresp
onse
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edNon
linea
rrelation
ships
(polyn
omial)canbetested
via
mod
elselectionan
dread
ily
grap
hed
Sh
apeof
theresp
onse
canbe
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Dostressors
combin
ead
ditivelyorin
tera
ctively
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edInteractionterm
scanbetested
forinclusion
viamod
elselection
procedures
Interactioneffectscanbeestimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rceof
inputmay
bequalitativeinform
ationor
empirical
mod
els)
Coe
fficien
tsgraphscanbeusedto
catego
rize
synergisticve
rsus
antago
nisticeffects
What
isth
eove
rallim
pac
tofcu
mulative
effectson
populations
Xmdash
Only
based
onqualitative
data
Based
onloss
ofsu
itab
lehab
itat
Sp
eciesndashhab
itatndashstressor
mod
els
canbeusedto
predictan
dco
mpare
speciesab
undan
cemea
sures
amon
gldquon
o-disturban
cereferen
cerdquo
curren
tan
dfuture
landscap
eco
nditionsbased
onGISla
ndscap
esimulation
Po
pulation
resp
onseoutputcanbe
compared
amon
gldquon
o-disturban
ce
referencerdquocu
rren
tan
dfuture
scen
arioswithmod
elinput(hab
itat
stressor
amou
nts)b
ased
onGIS
landscap
esimulation
Sp
eciesndashhab
itat
mod
elprediction
sforhab
itat
amou
nts
orsp
ecies
abunda
nce
canbe
compa
redam
ong
ldquono-disturban
cereferen
cerdquocu
rren
tan
dfuture
landscapecondition
sba
sedon
GISla
ndscapesimulation
What
isth
eove
rallim
pac
tonth
eco
mmunity
Xmdash
Only
based
onqualitative
data
Req
uires
calculatingspeciesndashhab
itat
mod
elsforallspeciescalculating
predicted
abunda
ncesin
reference
curren
tor
future
landscapesand
comparingcommunitymetrics
amon
gthesepredictedcommun
ities
Withsu
fficien
tdata
community
metrics
canbeco
mpared
amon
glowh
ighstressor
catego
ries
Req
uires
calculatingpop
ulation
resp
onsesforsp
eciesin
reference
curren
tor
future
landscap
esa
nd
comparingco
mmunitymetrics
amon
gthesepredictedco
mmunities
Com
munitymetrics
cansimilarly
becompa
redusingspeciesabun
dance
inreference
curren
tor
future
landscap
esa
spredictedfrom
empiricalspeciesndashhab
itatndashstressor
mod
els
How
certainar
eresu
lts
How
cance
rtainty
be
assessed
Verylow
mdashSu
bjectivenature
mak
esitdifficu
ltto
quan
tify
certainty
Verylowmdash
Ifspeciesndashhab
itat
basedon
qualitativeinform
ation(subjective
nature
mak
esitdifficu
ltto
quan
tify
certainty)
Highmdash
Statisticalsignificance
ofstressor
effectscanbeestimated
viaco
nfiden
ceintervalsof
param
eter
estimates
Moderateto
highmdash
Dep
endson
extent
towhichmod
elsareba
sedon
qualitativeversusem
piricalda
ta
Mod
eratemdash
Ifspecies
habitat
relation
shipsbasedon
empiricald
ata
Approachallowsuncertainty
ofrelation
shipsto
bequan
tified
and
inputinto
themod
els
Ran
geofstressors
evaluated
Highmdash
Not
limited
bysp
atial
dataon
stressors
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
andaccessible
spatiald
ata
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
and
accessible
spatiald
ata
Highmdash
Not
necessarily
limited
by
spatiald
ataon
stressorsifmod
els
based
onqualitativerelation
ships
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In more recent and complex approaches empirical speciesndashhabitat models are developed by sampling terrestrial speciesacross habitat types in a study area and using advanced statisticalanalyses to quantify species associations with different habitatsModels are termed resource selection functions (RSF) when spe-cies data (presenceabsence usedunused) are analyzed at smallwithin-population scales and species distribution models (SDM)or species abundance models (SAM) when data (occurrence abun-dance respectively) are analyzed at large range-level scales (Guisanand Zimmermann 2000 Guisan and Thuiller 2005 Elith andLeathwick 2009 Hegel et al 2010) For terrestrial birds current andemerging methods can be followed to convert species counts at asample station (the most common sample metric for terrestrialbirds) to a density estimate (Soacutelymos et al 2013 Matsuoka et al2014) Reporting species responses as densities (versus relativeabundance) provides (i) a relevant and readily communicatedmeasure of species response (ii) a standardized measure acrossregions and studies and (iii) an evaluation against conservationor management targets or population viability estimatesA range of statistical modeling frameworks are available to analyse
speciesndashhabitat relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) Typically habitat coefficients in these modelsare used to assign habitat rankings for species Both expert andempirically based habitat rankings require validation againstexternal data (or internal validations in the case of empiricalmodels) to ensure their reliability for an area (Pearce and Ferrier2000 Boyce et al 2002 Muir et al 2011) Once validated modelresults are used to assess the amount and distribution of suitablehabitat for species within a GIS (Millspaugh and Thompson 2009)Additionally when species responses have been converted to den-sity estimates coefficients can be coupled with GIS maps to esti-mate species densities or population sizes Within the context ofCEA the footprint or area of proposed projects existing projectsand disturbances and future land use scenarios can be overlaidon these maps to estimate the change in suitable habitat andassess effects on terrestrial species based on these habitat changeswithin the study area
322 Examples of applicationsWe highlight both project-based CEA and effects-based CEA
examples to demonstrate the flexibility of the habitat supplymethod Project-based CEAs typically use literature-based or hab-itat suitability models to identify map and quantify the area ofsuitable habitat for a given species within the study area We pro-vide a hypothetical example to explain how the habitat supplymethod is typically used to assess project-specific effects (Table 6)Changes in the amount of suitable habitat during the project(application phase eg from 10 to 6 of study area in Table 6)and after the project (closureremediation phase eg 0 changein Table 6) are estimated relative to current or baseline The hab-itat supply method is not typically used to assess CE because theextent to which existing or past stressors have affected pre-existinghabitat types is often not known Instead CE requirements areoften met by comparing the total area of the proposed project (eg5 of study area in Table 6) to the total area of other stressors regard-less of habitat affected (eg 35 Table 6) Thus in high or moder-ately disturbed areas these comparisons may suggest that a projectwould have minimal added effects relative to existing stressorsWhere data and resources allow some project-based CEAs mayuse historic maps or create hypothetical no-disturbance land-scapes within a GIS (eg see methods in ABMI 2017 Mahon et al2019) to assess how other stressors may have affected specific habi-tats However resource requirements (time data technical exper-tise) place this type of assessment beyond themandate or capacity ofmost project-based CEAs We include an example of the habitat sup-ply method from a recent project-based CEA from the IAAC RegistryT
able
4(conclud
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
inco
rpora
tetrad
itional
knowledge
Yes
Notusu
ally
Notusu
ally
Yes
Statistica
ltech
nical
expertise
required
Low
tomoderatemdash
Mod
eratewith
form
almod
eling
Moderate
High
Highto
very
high(specialized)
Tim
efra
me
Month
s
Month
sto
ltyea
rmdash
For
speciesndashhab
itat
mod
elsbased
onqualitativedata(dep
enden
ton
statusof
spatiald
ata
detailo
fthe
mod
ela
vailab
ilityof
species
inform
ation)
gtYea
rto
multi-ye
ar
Speciesdataco
llection
may
nee
dto
occu
rov
ergt1ye
arto
provide
sufficien
tsamples
Month
sto
gtmulti-ye
armdash
Highly
dep
enden
ton
sourceof
datainput
(existingdata
new
empirical
data
collection
)
Statisticalm
odel
dev
elop
men
tan
drefinem
entcanbetime
consu
ming
Modelingap
proachmay
require
additional
wee
ksor
month
s
gtYea
rmdash
Forem
piricalspe
ciesndashh
abitat
mod
elsthat
requ
irenew
data
collection
Dep
enden
ton
timerequired
toco
mpile
evaluatean
dcatego
rize
hab
itat
andstressor
data
Mahon and Pelech 209
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r pe
rson
al u
se o
nly
Tab
le5
Exa
mple
ofaqualitativereview
(matrixtable)for
aproject-based
CEA
Valued
compon
ent
Potential
effects
Mitigation
compen
sation
Residual
effect
Sign
ifican
ceof
residual
effects
Eco
logical
societal
value
Ove
rallsign
ifican
ceMag
nitude
Spatiale
xten
t
Freq
uen
cy
probab
ility
Duration
Rev
ersibility
Graywolf
Assoc
iatedwith
major
preysp
ecies
(moo
sed
eer)A
t
closure
increa
sed
area
sof
grasslan
d
willc
reatewhite-
tailed
dee
rhab
itat
andpoten
tially
higher
dee
ran
d
wolfnumbers
Limitfootprintof
disturbed
area
s
Increase
indeeran
d
wolfp
opulation
s
over
timeResidual
adverseeffect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
highqualitymoo
se
hab
itattherefore
limited
wolf
pop
ulation
MIN
IMAL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
is
long-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
is
reversibledu
ring
site
reclam
ation
Moo
sefollow
edby
wolvessh
ould
return
tosite
at
curren
tlevels
LOW
Wolvesarecommon
inthelocaland
region
alstudy
areasLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
wolve
s
aremob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
American
black
bea
r
Initialm
igration
duringsite
preparationan
d
constru
ction
return
tosite
area
afterperiodof
human
noise
hab
ituation
Limitfootprintof
disturbed
areas
Establishwildlife
policyto
minim
ize
human
interaction
withwildlifean
d
decrease
poten
tial
forhab
ituation
Increa
sedseason
al
use
ofthesite
over
timedep
endson
succession
of
reclaimed
area
s
Residual
adve
rse
effect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
abov
eav
erag
ebea
r
hab
itatM
INIM
AL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
islong-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
reversibledu
ring
site
reclam
ation
New
grasslan
d
hab
itat
may
increase
the
season
alhab
itat
use
bybe
ars
LOW
Bearde
nsity
inthe
study
site
islikely
comparab
leto
that
inthesurrou
nding
landscapeLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
bea
rsare
mob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
NoteT
hetable
summarizes
theproposed
project
effectsmitigationan
dco
mpen
sation
mea
surespoten
tial
residual
effects(theen
vironmen
tale
ffectof
aproject
that
remainsaftermitigationmea
sureshav
ebee
nim
plemen
ted)sign
ificance
ofresidual
effectsin
considerationof
ava
rietyof
assessmen
tfactors(ratings
inboldface
type)a
ndwhether
asign
ificantad
verseen
vironmen
tale
ffecton
ava
lued
compon
entisex
pected
asaresu
ltof
project
implemen
tation
Sou
rceEnvironmen
talImpactStatem
entfortheMarathon
PGM-CuProject20
12Thetable
represents
description
san
dratings
summarizingtheen
vironmen
tale
ffects
ofametal
minedev
elop
men
ton
twova
lued
compon
ents
(graywolfan
dAmerican
black
bea
r)
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(Environmental Impact Statement for the Springbank Off-streamReservoir Project 2018) to estimate area changes in the amountof suitable habitat (based on habitat suitability models) for selectVCs Spraguersquos Pipit (Anthus spragueii) and Northern Leopard Frog(Lithobates pipiens) during different flood scenarios created by waterdiversion to a channel and reservoir within the proposed projectarea (Table 7)Effects-based CEAs typically involve the creation and application
of empirical speciesndashhabitat models within the study area Maceet al (1999) evaluated the CE of human and resource activity ongrizzly bear (Ursus arctos) habitat inwesternMontana during springsummer and fall seasons The authors modelled bear resourceselection from telemetry data satellite imagery elevation humanactivity points roads and trails and extrapolatedmodels to a largerregion with similar climate and vegetation Quantifying and map-ping areas with a high probability of use by bears in each seasonwas used to inform conservation and management actions (egroad or trail closures within important seasonal habitats habitatrestoration future road creation) Roumldder et al (2016) outline aninnovative approach to integrate SDM and potential connectivitymodels to identify connected and isolated populations and habitatcorridors for a species of conservation concern the Sand Lizard(Lacerta agilis) in western Germany (Fig 2) Quantifying potentialconnectivity areas (for movement like dispersal or migration) wasused to identify areas of strong connectivity persistent isolationand stable connective networks needed to connect populationsThis proposed framework may be particularly useful for proposeddevelopments that affect (i) portions of a previously larger inter-connected population or (ii) connective features between perma-nently colonized habitat patches Additional examples from thepublished literature are listed in Table 3
323 Advantages and limitations CEA questions and whenwhyto use3231 AdvantagesAdvantages of the habitat supply method include its ability to
quantify effects using loss of suitable habitat This approach
which has wide-spread application in wildlife research may besuitable when (i) data to map and measure habitat change areavailable but empirical data on species response to specific stres-sors are limited (ii) collaborative approaches focused on localissues and values are important (eg speciesndashhabitat associa-tions may be based on both qualitative (TK local and expertknowledge) and quantitative (speciesndashhabitat models) data) and(iii) data to develop empirical speciesndashhabitat models and adjusteddensity estimates are available so that total impacts can be com-pared to current and future population targets (eg Mahon et al2014)
3232 LimitationsLimitations to the habitat supply modelling approach include
the availability of accurate publicly available spatial data forhabitats and stressors and reliable specieshabitat associationsfrom the region of interest If additional data are required torefine existing habitat suitability models or create new empiricalspeciesndashhabitat models within a study area stakeholders or part-ners should a priori develop rigorous sampling designs and sur-vey protocols to meet project objectives We note two additionalcautions when using this approach Specieshabitat associationsbased on limited empirical data from within the region or datafrom outside the region require model validation and testing(Muir et al 2011) Finally stressor effects are limited to how stres-sors contribute to the loss of suitable habitat (habitat changesare used to infer population consequences) Habitat-based analy-sis alone may not be useful because (i) habitat is assumed to bea useful measure of population status (ii) habitat needs of the tar-get species or VC are assumed to be known (Schultz 2010) and(iii) specific stressor effects on the target species or VC are notquantified (eg the shape magnitude and direction of speciesrsquoresponse to stressors indirect or novel effects of stressors on speciesor interactions among stressors) Failure to identify which stressorsare influencing target species or VCsmay limit future research intobiological mechanisms (eg habitat selection movement disper-sal reproduction survival predatorndashprey dynamics) needed to
Fig 1 Example of a qualitative review (conceptual modeldiagram) for an effects-based CEA Figure represents an annual life cycle ofevents of Greater Sage-Grouse responding to oil and gas development in southwest Wyoming USA (from Heinrichs et al 2019 reproducedwith permission of Ecol Appl Vol 29 copy2019 Ecological Society of America)
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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
212 Environ Rev Vol 29 2021
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
218 Environ Rev Vol 29 2021
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
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tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
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Austin M 2007 Species distribution models and ecological theory a criticalassessment and some possible new approaches Ecol Modell 200 1ndash19 doi101016jecolmodel200607005
Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
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Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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iron
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n 12
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r pe
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se o
nly
Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
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Canadian Environmental Assessment Agency (CEAA) Expert Panel Review 2017Building common ground a new vision for impact assessment in Canada Thefinal report of the expert panel for the review of Environmental assessmentprocesses Available from httpswwwcanadacaenservicesenvironmentconservationassessmentsenvironmental-reviewsenvironmental-assessment-processesbuilding-common-groundhtml [last accessed 10 April 2020]
Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
Environmental Impact Statement for the Springbank Off-stream ReservoirProject 2018 Environmental Impact Assessment of the Springbank Off-stream Reservoir Project Stantec Incorporated Available from httpsiaac-aeicgcca [last accessed 15 June 2020]
Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
Impact Assessment Agency of Canada (IAAC) 2019 The Impact AssessmentProcess Timelines and Outputs Available from httpswwwcanadacaenimpact-assessment-agencyhtml [last accessed 20 August 2020]
Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
Johnson C 2016 Understanding the spatial responses of caribou to human-caused disturbance In Development of modeling tools to address cumulativeeffects on the summer range of the Bathurst caribou herd ndash a demonstrationproject Edited by JS Nishi and A Gunn Environment and Natural ResourcesGovernment of the Northwest Territories Yellowknife NT pp 13ndash36
Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
Millspaugh JJ and Thompson FR III 2009 Models for planning wildlifeconservation in large landscapes Elsevier Amsterdam the Netherlands
Morrison ML Marcot BG and Mannan RW 2006 Wildlife-habitat rela-tionships concepts and applications 3rd ed Island Press WashingtonDC USA
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
Noble B Liu J and Hackett P 2017 The contribution of project environmentalassessment to assessing and managing cumulative effects individually andcollectively insignificant Environ Manage 59 531ndash545 doi101007s00267-016-0799-7 PMID27885387
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Pearce J and Ferrier S 2000 Evaluating the predictive performance ofhabitat models developed using logistic regression Ecol Modell 133(3)225ndash245 doi101016S0304-3800(00)00322-7
Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
Salice CJ 2012 Multiple stressors and amphibians Contributions of adversehealth effects and altered hydroperiod to population decline and extinctionJ Herpetol 46(4) 675ndash681 doi10167011-091
Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
Sawyer H Kauffman MJ and Nielson RM 2009 Influence of well pad ac-tivity on winter habitat selection patterns of Mule Deer J Wildl Manage73(7) 1052ndash1061 doi1021932008-478
Schieck J Muhly T Huggard D Solymos P Pan D Heckbert S andBayne E 2014 Predicting the cumulative effects of human developmenton biodiversity in northeastern Alberta Petroleum Technology AllianceCanada (Alberta Upstream Petroleum Research Fund) Calgary AB Avail-able from httpsauprfptacorgecological-2a-tool-to-assess-cumulative-effects-of-development-on-biodiversity [last accessed 13 November 2018]
Schultz C 2010 Challenges in connecting cumulative effects analysis toeffective wildlife conservation planning BioScience 60(7) 545ndash551 doi101525bio201060710
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Silva JP Santos M Queiroacutes L Leitatildeo D Moreira F Pinto M et al2010 Estimating the influence of overhead transmission power lies andlandscape context on the density of little bustard Tetrax tetrax breedingpopulations Ecol Modell 221 1954ndash1963 doi101016jecolmodel201003027
Mahon and Pelech 223
Published by NRC Research Press
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Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
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Swor T and Canter L 2011 Promoting environmental sustainability via anexpert elicitation process Environ Impact Assess Rev 31 506ndash514 doi101016jeiar201101014
Tremblay JA Boulanger Y Cyr D Taylor AR Price DT and St-Laurent MH2018 Harvesting interacts with climate change to affect future habitat qualityof a focal species in eastern Canadarsquos boreal forest PLoS ONE 13 e0191645doi101371journalpone0191645 PMID29414989
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US Fish and Wildlife Service (USFWS) 1981 Standards for development ofhabitat suitability models 103 ESM US Department of the Interior Fishand Wildlife Service Washington DC
Usher PJ 2000 Traditional ecological knowledge in environmental assess-ment and management Arctic 53 183ndash193 doi1014430arctic849
Uusitalo L 2007 Advantages and challenges of Bayesian networks in environ-mental modelling Ecol Modell 203 312ndash318 doi101016jecolmodel200611033
Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
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224 Environ Rev Vol 29 2021
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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtDSCReportingLevel 0PDFXOutputConditionIdentifier ()PDFXCompliantPDFOnly falseAllowTransparency falsePreserveCopyPage trueUsePrologue falseStartPage 1MonoImageDownsampleThreshold 10GrayImageDownsampleThreshold 10CheckCompliance [None]CreateJDFFile falsePDFXSetBleedBoxToMediaBox trueEmbedOpenType falseOPM 0PreserveOverprintSettings falseUCRandBGInfo RemoveColorImageDownsampleThreshold 10MonoImageDict ltltK -1gtgtGrayImageDownsampleType AverageDescription ltltENU (Use these settings to create Adobe PDF documents suitable for reliable viewing and printing of business documents Created PDF documents can be opened with Acrobat and Adobe Reader 50 and later)PTB 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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltFEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020be44c988b2c8c2a40020bb38c11cb97c0020c548c815c801c73cb85c0020bcf4ace00020c778c1c4d558b2940020b3700020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002egtNLD (Gebruik deze instellingen om Adobe PDF-documenten te maken waarmee zakelijke documenten betrouwbaar kunnen worden weergegeven en afgedrukt De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 50 en hoger)NOR 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 (Utilizzare queste impostazioni per creare documenti Adobe PDF adatti per visualizzare e stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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 ltFEFF4f7f75288fd94e9b8bbe5b9a521b5efa7684002000410064006f006200650020005000440046002065876863900275284e8e55464e1a65876863768467e5770b548c62535370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c676562535f00521b5efa768400200050004400460020658768633002gtSUO 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 ltFEFF4f7f752890194e9b8a2d7f6e5efa7acb7684002000410064006f006200650020005000440046002065874ef69069752865bc666e901a554652d965874ef6768467e5770b548c52175370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c4f86958b555f5df25efa7acb76840020005000440046002065874ef63002gtgtgtCropMonoImages trueDefaultRenderingIntent RelativeColorimetericPreserveHalftoneInfo falseColorImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtCropGrayImages truePDFXOutputCondition ()SubsetFonts trueEncodeMonoImages trueCropColorImages truePDFXNoTrimBoxError truegtgtsetdistillerparamsltltPageSize [61207920]HWResolution [600600]gtgtsetpagedevice
There are multiple CEA definitions frameworks (see Duinkeret al 2013) processes and questions (as described previously andin Table 1) In this review we define key science-based CEA objec-tives relevant to both project-based and effects-based CEAs as thefollowing (i) describe or characterize overall CE (ii) determinethe rank or magnitude and direction (eg positive or negative) ofindividual stressor effects (iii) describe the shape of the relation-ship between stressors and species (eg linear or nonlinear)(iv) determine how stressors combine to affect species (eg addi-tive interactivendashsynergistic interactivendashantagonistic compensa-tory or masking) (v) relate CE to baseline or pre-disturbanceconditions defined thresholds or management triggers and(vi) describe or assign certainty or probability of occurrence ofpredicted effects Within our review we assess the capacity ofCEA methods for species in terrestrial systems to address thesescience-based CEA objectives
3 Description and evaluation of CEAmethodsHere we provide an overview of current methods for project-
based and effects-based CEAs that have or could be used for terres-trial species We conducted a three-stage review to (i) identify CEAmethods used in project-based CEAs and effects-based CEAs and(ii) document relevant examples First we reviewed all active assess-ments (as of 15 June 2020) listed on the Canadian Impact AssessmentRegistry (httpsiaacgcca) including Environmental Assess-ments under Canadian Environmental Assessment Act 2012 (n = 50)Environmental Assessments by Review Panel under CanadianEnvironmental Assessment Act 2012 (n = 8) Impact Assessmentsunder Impact Assessment Act 2019 (n = 2) and Regional Assessmentsunder Impact Assessment Act 2019 (n = 2) and recorded the types ofmethods used within the CEA (eg Environmental ImpactStatement Impact Statement Regional Assessment) in Table 2Second we searched for published reviews of CEA methods
within the scientific literature and the CEA guidance literature(Table 2) We collapsed similar approaches identified in stages 1and 2 into four broad categories of CEA methods qualitativereview habitat supply models empirical speciesndashstressor modelsand decision support models (focusing on Bayesian belief network(BBN) models) We distinguished data products like indicators (iea measurable variable chosen to represent the state of a compo-nent) and indices (ie composite numerical value for some envi-ronmental attribute usually comprised of two or more variables)that can be used within a CEA process from analytical CEAmethods We also distinguished data tools like geographic in-formation systems (GIS) used to produce spatial data productsincluded within most modern project-based or effects-basedCEAs from analytical CEAmethodsThird we conducted targeted searches using Web of Science
across the four CEA methods to find published examples for spe-cies in terrestrial systems We include some CEA publishedpapers as examples in the description of each CEAmethod in sub-sequent sections and list all papers reviewed by CEA methodincluding study area location and target terrestrial species orgroup (Table 3) CEA methods fall on a gradient of increasingcomplexity data requirements and technical expertise In manycases a combination of CEA methods will be required to effec-tively answer science-based CEA questions Our intent is not anexhaustive review of all CEA methods or an intensive review ofone category of methods Instead we describe each CEA methodand provide relevant examples We also identify advantages andlimitations explain how methods address science-based CEAquestions (Table 4) and provide direction on when and why touse specific CEAmethods
31 Qualitative review
311 Description of methodThis broad category of methods includes a variety of approaches
to collect (questionnaires interviews workshops literature reviews)
and summarize (checklists matrices qualitative or conceptualmodelsdiagrams) qualitative data or information (eg expertopinion published or unpublished information) as an exclusiveor primarymeans to assess CE (Cocklin et al 1992 Smit and Spaling1995 Duinker et al 2013 CEAA 2014 2018 see Tables 2 and 3)rather than direct measurements of species response to habitatchange or stressors This approach would typically include assess-ments with high constraints on data availability (for species habi-tats and stressors) time and (or) cost For example informationon stressors may be based on qualitative descriptions or approxi-mations of development (eg hard copies of maps written orverbal descriptions of proposed developments or stressors) ratherthan spatial and attribute data on stressors within a GIS In somecases qualitative datainformation is ranked to provide a categori-cal assessment of effects (eg using literature or expert review torankweight potential effects of stressors on species) Formal con-sensus techniques and qualitative modelling can also be used (egDelphi method see Linstone and Turoff 2002 fuzzy cognitivemapping see Gray et al 2015) although these remain infrequentfor most assessments of terrestrial species Although also a formof qualitative review we consider independent traditional knowl-edge (TK) studies or combined TK and scientific knowledge (SK) stud-ies (eg co-production of knowledge) to be separate from thiscategory due to distinct methods used to select experts with dif-ferent types of knowledge (eg local harvesters and huntersElders government employees scientists) and conduct the elici-tation procedure (Huntington 1998 Usher 2000 Polfus et al 2014Beacutelisle et al 2018)Qualitative review is common among project-based CEAs for
terrestrial species (see Table 2 stage 1) where proponents typi-cally conduct literature reviews to collect information and usechecklists and matrices to rank potential effects on VC (eg lowto high) Qualitative review is less common among effects-basedCEAs for terrestrial species (outside of TK studies) with the excep-tion of (i) potential stressor effects that are difficult to map orquantify (eg pesticide use pollution species interactions) and(ii) complex systems where multiple stressor effects are difficultto visualize or comprehend In the latter case conceptual modelsdiagrams can be combined with other quantitative CEA approaches(eg habitat supply empirical speciesndashstressor and decision supportmodels) where the conceptual model is shared refined and usedto guide monitoring and research questions and hypothesesamong partners (Marcot et al 2006 Lindenmayer and Likens 2010Nelitz et al 2015)
312 Examples of applicationsWe highlight two examples of qualitative review methods and
list additional examples from the published literature in Table 3The first example from a recent project-based CEA from theIAAC Registry (Environmental Impact Statement for the MarathonPGM-Cu Project 2012) uses a matrix table (Table 5) to identify theVCs potential effects mitigation measures and residual effectsof the project For each VC gray wolf (Canis lupus) and Americanblack bear (Ursus americanus) a rank or level is assigned to eachparameter used to characterize residual effects (magnitude spa-tial extent frequency duration reversibility ecological or soci-etal value) These levels are then combined to create a measureof overall significance a required first step in conducting theCEA (CE are defined as the environmental effects resulting fromthe project in combination with other projects or activities) Thesecond example from an effects-based CEA uses a conceptualmodeldiagram to illustrate the Greater Sage-Grouse (Centrocercusurophasianus) annual life cycle including influence of oil and gasdevelopment on site fidelity dispersal and demography (Fig 1)The life cycle informs the population simulation model used totrack population dynamics and habitat use of simulated Sage-Grousethrough space and time as they respond to changing landscape
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conditions (climate-induced vegetation change) and exposure tooil and gas stressors (Heinrichs et al 2019)
313 Advantages and limitations CEA questions and whenwhyto use
3131 AdvantagesThe advantage of qualitative review is its utility when quantita-
tive data on habitat and stressors and time and (or) financialresources are limited The approach also has the ability to (i) offera collaborative flexible and accessible approach when localissues and values are important (eg TK or combined TK and SKstudies) (ii) identify a wide range of stressors and mechanisms
(eg indirect effects species interactions stressors that cannotbe effectively mapped) and (iii) serve as a conceptual frameworkwithin a CEA using one or more quantitative methods to assessand (or) simulate multiple stressor effects (eg empirical speciesndashstressor models Bayesian belief network (BBN) models simulationmodels)
3132 LimitationsThe limitation of qualitative review is uncertainty surrounding
the objectivity and reliability of assessments particularly whenexpert opinion is applied to novel locations or stressors Specifi-cally these limitations include the inability to quantify any stres-sor related impacts including effects on habitat area (eg amount
Table 2 Cumulative effects assessment (CEA) methods identified in the stage one review (Canadian Impact Assessment Registry) and the stagetwo review (published reviews of CEAmethods and publicly available CEA guidance documents)
Stage CEAmethods
Stage 1 Canadian Impact Assessment Registry Total 2262 (35) with Environmental Impact Statement ImpactStatement Regional Assessment Report
Environmental Assessment under Canadian Environmental Assessment Act2012 (n = 50)
1650 (32) with Environmental Impact Statement1516 used QR416 used QR and HSM
Environmental Assessment by Review Panel under CanadianEnvironmental Assessment Act 2012 (n = 8)
58 (625) with Environmental Impact Statement55 used QR35 used QR and HSM15 used QR HSM and SM
Impact Assessment under Impact Assessment Act 2019 (n = 2) 02 (0) with Impact StatementRegional Assessment under Impact Assessment Act 2019 (n = 2) 12 (50) with Regional Assessment Report
1 used QR HSM and DSM
Stage 2 Reviews of CEAmethodsCocklin et al 1992 Environmental checklists matrix approaches inputndashoutputmethods
approaches including network analysisCanter and Atkinson 2011 Indicators and indices habitat suitability modelsDuinker et al 2013 GIS scenarios thresholds indicators and indices network analysis
matrices habitat suitability modelshabitat selection modelshabitat supply models stressor-based and effects-based approachesmultivariate statistical analysis air pollutionmodels landscapemodels animal population simulation models water-basedsimulationmodels public engagement community-basedmonitoring population viability analysis costndashbenefit analysisvisual amenity analysis traffic-light decision support systemsustainability appraisal
Hodgson and Halpern 2019 Experiment meta-analysis single species models mappingqualitative models multi-species models (aquaticmarine)
Marcot et al 2001 Marcot et al 2006 McCann et al 2006 Uusitalo 2007 Bayesian networkmodelsBayesian belief network (BBN) models
Stage 2 CEA guidance documentsTechnical Guidance for Assessing CEA under Canadian EnvironmentalAssessment Act 2012 Draft (CEAA 2014) Technical Guidance forAssessing CEA under Canadian Environmental Assessment Act 2012InterimGuidance (CEAA 2018)
Questionnaires and interviews checklists and matrices network andsystems analysisdiagrams indicators and indices conceptual andnumerical models trend analysis spatial analysis
Section 22-Factors to be considered (IAAC 2019 httpswwwcanadacaenimpact-assessment-agency)
New IAAC guidance in preparation interim guidance is CEAA (2018)
Consideration of cumulative effects in YESAB Assessments (YESAB 20052019)
No CEAmethods
Environmental Assessment Guidelines (MVEIRB 2004) No CEAmethodsProponents Guide NIRB Technical Guide Series (NIRB 2020) No CEAmethods
Note Agency definitions CEAA Canadian Environmental Assessment Agency IACC Impact Assessment Agency of Canada administers projects under CanadianEnvironmental Assessment Act 2012 and Impact Assessment Act 2019 YESAB Yukon Environmental and Socio-economic Assessment Board administers projects underYukon Environmental and Socio-economic Assessment Act 2003 MVEIRB Mackenzie Valley Environmental Impact Review Board administers projects underMackenzie ValleyResource Management Act 1998 Nunavut Impact Review Board (NIRB) administers projects under Nunavut Planning and Project Assessment Act 2013 CEA methoddefinitions QR qualitative review HSM habitat supply models SM simulation models DSM decision support models
Mahon and Pelech 205
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of suitable habitat lost) and direct (eg habitat loss mortality) orindirect stressor effects on species (eg altered habitat selectionreduced demographic rates like reproduction or survival)
3133 Science-based CEA questionsSee Table 4 to identify how qualitative review methods address
key science-based CEA questions
3134 Whenwhy to usemdash project-based CEAsldquoWhen to userdquo when quantitative data on species and spatial
data on habitats or stressors limit the development of speciesndashhabitatmodels (to assess change in habitat) and (or) speciesndashhabitatndashstressor models (to assess species response to habitat and stressors)Qualitative data and qualitative reviewmethods can be used to iden-tify rank and categorize stressor effects ldquoWhy to userdquo allows for acomprehensive listing and ranking of potential stressor effects onVCs in relation to (i) mitigation measures for the proposed projectand (ii) other past current or future projects
3135 Whenwhy to usemdash effects-based CEAsldquoWhen to userdquo when systems are complex conceptual diagrams
can be combined with other quantitative methods and used to(i) identify multiple stressors and (or) pathways of effects on dif-ferent life stages of target species or VCs (see Fisher and Burton2018 Pirotta et al 2018 and Heinrichs et al 2019 for examples)and (ii) outline the analysis process or framework (see Munns2006 and Katzner et al 2020 for examples) ldquoWhy to userdquo allowsfor a visual representation of the system (stressors ecologicaldrivers individual and population responses) and the analysisprocess
32 Habitat supply models
321 Description of methodIn this category we include various forms of speciesndashhabitat
modelling currently the most common approach to terrestrialspecies assessment in most project-based CEAs as well as in con-servation planning and landscape management (eg Morrisonet al 2006) Approaches vary in complexity from simple literature-based habitat rankings to complex statistical models of empirical
speciesndashhabitat relationships (see Table 3) A defining characteristicof this method for CEA is that habitat is used as a surrogate for theidentified terrestrial species (or as a VC itself) For habitat supplymodels quantitative changes in the amount and (or) quality of aspeciesrsquo habitat (typically categorized and measured within a GIS)are used as the primary measure of CE In contrast for empiricalspeciesndashstressormodels the species direct response to one ormorestressors is used as the primary measure of CE We provide morescientific background to this approach subsequently however ingeneral this approachwith respect to CEA typically involves
1 Developing speciesndashhabitat associations or models Thisrequires ranking the suitabilityeffectivenessfunctionality ofmeasurable habitat categories for target species using expertopinion literature review and (or) empirical speciesndashhabitatcorrelations
2 Quantifying changes in the amount of this habitat as a resultof the proposed project or projects and existingfuture stressors
3 Inferring effects on species as a result of these habitat changes
Speciesndashhabitat models can be developed using a variety ofexpert opinion and statistical modelling approaches have a longhistory in wildlife management and landscape planning and arewell-documented in the literature (eg Morrison et al 2006Millspaugh and Thompson 2009 Guisan et al 2013) Standardsfor developing literature and expert opinion based habitat suit-ability indices (HSI) date back to the 1980s (US Fish and WildlifeService (USFWS) 1980 1981) and many jurisdictions have simi-larly developed protocols for wildlife habitat rankings in Canada(BC Resource Inventory Committee 1999 Clarke 2012) Thesehabitat suitability models typically use experts and (or) compilespecies information from existing literature to rank habitats fora species (eg on a 0ndash1 scale or similar categorical ranking sys-tem) Additional variables that may modify habitat quality (egproximity to roads) are also often considered to differentiatebetween habitat capability (the potential of the habitat to sup-port the species under optimal conditions) and habitat suitabilityor effectiveness (the actual ability of the habitat to support the spe-cies in a specific study area)
Table 3 Cumulative effects assessment (CEA) methods identified in the stage three review (targeted reviews of CEA methods) qualitativereview habitat supply models empirical speciesndashstressor models and decision support models (Bayesian belief network (BBN) models)
Stage 3 Targeted reviews of CEAmethods References
Qualitative review Chilima et al 2013 (Ontario CAN watersheds) Cooper 2011 (United Kingdom multiple species)Swor and Canter 2011 (Pennsylvania USA water fish mussels riparianfloodplain resources)
Habitat supply models Canter and Atkinson 2011 (USA multiple species) Houle et al 2010 (Quebec CAN gray wolf)Johnson et al 2005 (Northwest Territories CAN caribou gray wolf grizzly bear wolverine) Maceet al 1999 (Montana USA grizzly bear) Roumldder et al 2016 (Cologne Germany Sand Lizard)
Empirical species-stressor models Sawyer et al 2009 (Wyoming USA mule deer) Houle et al 2010 (Quebec CAN gray wolf) Salice et al2011 (South Carolina USA Eastern Narrow-mouthed Toad) Salice 2012 (southeast USA EasternNarrow-mouthed Toad) Wilson et al 2013 (northwest Alaska USA caribou) Goodale andMilman2019 (offshore regions waterfowl waterbirds landbirds) Hodgson et al 2017 (various locationsinsects birds fish ungulates) Jones et al 2017 (Muskoka RiverWatershed CAN benthic invertebrates)Fisher and Burton 2018 (northeast Alberta CAN gray wolf white-tailed deermoose American blackbear coyote Canada lynx fisher red fox snowshoe hare American red squirrel) Claireau et al 2019(western France 10 bat species) Daniel and Koper 2019 (southern Alberta CAN 5 grassland landbirdspecies) Heinrichs et al 2019 (Wyoming USA Greater Sage-Grouse) Mahon et al 2019 (northeastAlberta CAN 27 boreal landbird species) Katzner et al 2020 (California USA roadrunner hawk)Leston et al 2020 (northeast Alberta CAN 20 boreal landbird species)
McNay et al 2006 (north-central British Columbia CAN caribou) Steventon et al 2006 (north coastof British Columbia CAN Marbled Murrelet) Howes et al 2010 (eastern Australia woodlandlandbirds) MacCracken et al 2013 (Bering and Chukchi Seas Pacific walrus) Ban et al 2014 (GreatBarrier Reef Australia reef coral) Fortin et al 2016 (Alaska USA brown bears) Mantyka-Pringleet al 2016 (South East Queensland Australia macroinvertebrates fish) Mantyka-Pringle et al 2017(Slave River Delta Northwest Territories CAN fish waterfowl mammals)
206 Environ Rev Vol 29 2021
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Tab
le4
Summaryco
mparison
ofan
alytical
method
sforcu
mulative
effectsassessmen
t(CEA)for
terrestrialspecies
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Cumulative
effectsof
stressors
bas
edon
Exp
ertknow
ledge
literature
review
oflikelyeffects
Estim
ated
chan
gein
amou
ntof
suitab
lehab
itat
forsp
eciesas
aresu
ltof
stressorssu
itab
lehab
itat
isch
arac
terize
dusingex
pert
opinionliterature
and(or)
empirical
data
Directmea
suremen
tsof
speciesrsquo
resp
onse
tograd
ientof
stressors
andco
mbinationsof
stressors
Mod
eled
respon
seof
speciesab
unda
nce
orhab
itat
tocombined
hab
itatstressor
andother
inpu
ts(in
putsmay
bequ
alitativeor
empiricald
ata)
Most
commonly
asso
ciated
with
Project-based
CEA
Project-based
CEAscien
tific
research
Effects-based
CEAscien
tific
research
Effects-based
CEAscien
tificresearch
Key
strength
s
Limited
data
time
andco
strequirem
ents
Welle
stab
lish
edap
proach
Addresses
allo
rmostCEAgo
als
question
susingdirectmea
sures
ofsp
ecies
stressor
relation
ship
Can
incorporatemultipleda
tatype
san
dsources(qua
litativean
dqu
antitative
data)
Can
includeawiderange
ofstressors
Allow
squan
tification
ofeffects
(egloss
ofsu
itab
lehab
itat)w
hen
directsp
eciesndashstressor
resp
onse
orsp
eciesdataarelimited
Other
strengthslimitationsba
sedon
theex
tentto
whichqu
alitativeversus
quan
titative
dataareused
Key
limitations
Fa
ilsto
answ
erCEAquestion
sor
doe
sso
withuncertainreliab
ility
Inco
rporates
effectson
lydueto
hab
itat
loss
Highdata
timean
dco
strequirem
ents
Infreq
uen
tuse
interrestrialsystems
may
limitap
plication
Accuracy
andreliab
ilityhighly
dep
enden
ton
qualityan
dprecision
ofsp
ecies
hab
itat
associations
Restrictedto
speciesthat
can
beread
ilysampled
Fa
ilsto
address
man
yCEA
objectives
(interactionssh
ape
ofsp
eciesndashstressor
resp
onse)
Quan
titative
datarequirem
ents
Spec
ies
Nonemdash
Not
quan
titative
Lo
wmdash
Ifbased
onqualitative
species
hab
itat
associations
Veryhighmdash
Req
uires
species
metrics
across
fullgrad
ientof
stressors(andco
mbinationsif
testingforinteractions)
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareusedin
themod
el
Highmdash
Ifbased
onem
pirical
data
species
hab
itat
associations
(see
text)
Hab
itat
mdashsp
atiald
ata
Nonemdash
Not
quan
titative
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Stressormdash
spatiald
ata
Nonemdash
Not
quan
titative
Highto
very
highmdash
Tobe
effectiverequires
accu
rate
accessible
spatiald
ataon
ldquoindividualrdquostressorsoftenbroad
sector
catego
ries
(mining
oilg
as)
areused
Veryhighmdash
Tobeeffective
requires
accu
ratea
ccessible
spatiald
ataon
ldquoindividualrdquo
stressors
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
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Tab
le4(continu
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
answ
erkey
CEAquestionsan
dap
plica
tionofCEA
What
isth
erelative
mag
nitudean
ddirection
ofindividual
stressor
effects
Xmdash
Only
based
onqualitativedata
Based
onrelative
contribution
ofstressor
toreducingam
ountof
estimated
suitab
lehab
itat
(req
uires
ldquobackfillingrdquo
individual
stressorsin
GISsee
text)
Pa
rameter
estimates
inmod
elrepresentmag
nitudedirection
and
certainty
ofstressor
effects
Stressor
effectstypically
are
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Relativeco
ntribution
ofstressors
toloss
ofsu
itab
lehab
itat
canbe
analysed
(req
uires
ldquoback-fillingrdquo
individual
stressorsin
GISsee
text)
What
isth
esh
apeofth
esp
eciesndashstressorresp
onse
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edNon
linea
rrelation
ships
(polyn
omial)canbetested
via
mod
elselectionan
dread
ily
grap
hed
Sh
apeof
theresp
onse
canbe
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Dostressors
combin
ead
ditivelyorin
tera
ctively
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edInteractionterm
scanbetested
forinclusion
viamod
elselection
procedures
Interactioneffectscanbeestimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rceof
inputmay
bequalitativeinform
ationor
empirical
mod
els)
Coe
fficien
tsgraphscanbeusedto
catego
rize
synergisticve
rsus
antago
nisticeffects
What
isth
eove
rallim
pac
tofcu
mulative
effectson
populations
Xmdash
Only
based
onqualitative
data
Based
onloss
ofsu
itab
lehab
itat
Sp
eciesndashhab
itatndashstressor
mod
els
canbeusedto
predictan
dco
mpare
speciesab
undan
cemea
sures
amon
gldquon
o-disturban
cereferen
cerdquo
curren
tan
dfuture
landscap
eco
nditionsbased
onGISla
ndscap
esimulation
Po
pulation
resp
onseoutputcanbe
compared
amon
gldquon
o-disturban
ce
referencerdquocu
rren
tan
dfuture
scen
arioswithmod
elinput(hab
itat
stressor
amou
nts)b
ased
onGIS
landscap
esimulation
Sp
eciesndashhab
itat
mod
elprediction
sforhab
itat
amou
nts
orsp
ecies
abunda
nce
canbe
compa
redam
ong
ldquono-disturban
cereferen
cerdquocu
rren
tan
dfuture
landscapecondition
sba
sedon
GISla
ndscapesimulation
What
isth
eove
rallim
pac
tonth
eco
mmunity
Xmdash
Only
based
onqualitative
data
Req
uires
calculatingspeciesndashhab
itat
mod
elsforallspeciescalculating
predicted
abunda
ncesin
reference
curren
tor
future
landscapesand
comparingcommunitymetrics
amon
gthesepredictedcommun
ities
Withsu
fficien
tdata
community
metrics
canbeco
mpared
amon
glowh
ighstressor
catego
ries
Req
uires
calculatingpop
ulation
resp
onsesforsp
eciesin
reference
curren
tor
future
landscap
esa
nd
comparingco
mmunitymetrics
amon
gthesepredictedco
mmunities
Com
munitymetrics
cansimilarly
becompa
redusingspeciesabun
dance
inreference
curren
tor
future
landscap
esa
spredictedfrom
empiricalspeciesndashhab
itatndashstressor
mod
els
How
certainar
eresu
lts
How
cance
rtainty
be
assessed
Verylow
mdashSu
bjectivenature
mak
esitdifficu
ltto
quan
tify
certainty
Verylowmdash
Ifspeciesndashhab
itat
basedon
qualitativeinform
ation(subjective
nature
mak
esitdifficu
ltto
quan
tify
certainty)
Highmdash
Statisticalsignificance
ofstressor
effectscanbeestimated
viaco
nfiden
ceintervalsof
param
eter
estimates
Moderateto
highmdash
Dep
endson
extent
towhichmod
elsareba
sedon
qualitativeversusem
piricalda
ta
Mod
eratemdash
Ifspecies
habitat
relation
shipsbasedon
empiricald
ata
Approachallowsuncertainty
ofrelation
shipsto
bequan
tified
and
inputinto
themod
els
Ran
geofstressors
evaluated
Highmdash
Not
limited
bysp
atial
dataon
stressors
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
andaccessible
spatiald
ata
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
and
accessible
spatiald
ata
Highmdash
Not
necessarily
limited
by
spatiald
ataon
stressorsifmod
els
based
onqualitativerelation
ships
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In more recent and complex approaches empirical speciesndashhabitat models are developed by sampling terrestrial speciesacross habitat types in a study area and using advanced statisticalanalyses to quantify species associations with different habitatsModels are termed resource selection functions (RSF) when spe-cies data (presenceabsence usedunused) are analyzed at smallwithin-population scales and species distribution models (SDM)or species abundance models (SAM) when data (occurrence abun-dance respectively) are analyzed at large range-level scales (Guisanand Zimmermann 2000 Guisan and Thuiller 2005 Elith andLeathwick 2009 Hegel et al 2010) For terrestrial birds current andemerging methods can be followed to convert species counts at asample station (the most common sample metric for terrestrialbirds) to a density estimate (Soacutelymos et al 2013 Matsuoka et al2014) Reporting species responses as densities (versus relativeabundance) provides (i) a relevant and readily communicatedmeasure of species response (ii) a standardized measure acrossregions and studies and (iii) an evaluation against conservationor management targets or population viability estimatesA range of statistical modeling frameworks are available to analyse
speciesndashhabitat relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) Typically habitat coefficients in these modelsare used to assign habitat rankings for species Both expert andempirically based habitat rankings require validation againstexternal data (or internal validations in the case of empiricalmodels) to ensure their reliability for an area (Pearce and Ferrier2000 Boyce et al 2002 Muir et al 2011) Once validated modelresults are used to assess the amount and distribution of suitablehabitat for species within a GIS (Millspaugh and Thompson 2009)Additionally when species responses have been converted to den-sity estimates coefficients can be coupled with GIS maps to esti-mate species densities or population sizes Within the context ofCEA the footprint or area of proposed projects existing projectsand disturbances and future land use scenarios can be overlaidon these maps to estimate the change in suitable habitat andassess effects on terrestrial species based on these habitat changeswithin the study area
322 Examples of applicationsWe highlight both project-based CEA and effects-based CEA
examples to demonstrate the flexibility of the habitat supplymethod Project-based CEAs typically use literature-based or hab-itat suitability models to identify map and quantify the area ofsuitable habitat for a given species within the study area We pro-vide a hypothetical example to explain how the habitat supplymethod is typically used to assess project-specific effects (Table 6)Changes in the amount of suitable habitat during the project(application phase eg from 10 to 6 of study area in Table 6)and after the project (closureremediation phase eg 0 changein Table 6) are estimated relative to current or baseline The hab-itat supply method is not typically used to assess CE because theextent to which existing or past stressors have affected pre-existinghabitat types is often not known Instead CE requirements areoften met by comparing the total area of the proposed project (eg5 of study area in Table 6) to the total area of other stressors regard-less of habitat affected (eg 35 Table 6) Thus in high or moder-ately disturbed areas these comparisons may suggest that a projectwould have minimal added effects relative to existing stressorsWhere data and resources allow some project-based CEAs mayuse historic maps or create hypothetical no-disturbance land-scapes within a GIS (eg see methods in ABMI 2017 Mahon et al2019) to assess how other stressors may have affected specific habi-tats However resource requirements (time data technical exper-tise) place this type of assessment beyond themandate or capacity ofmost project-based CEAs We include an example of the habitat sup-ply method from a recent project-based CEA from the IAAC RegistryT
able
4(conclud
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
inco
rpora
tetrad
itional
knowledge
Yes
Notusu
ally
Notusu
ally
Yes
Statistica
ltech
nical
expertise
required
Low
tomoderatemdash
Mod
eratewith
form
almod
eling
Moderate
High
Highto
very
high(specialized)
Tim
efra
me
Month
s
Month
sto
ltyea
rmdash
For
speciesndashhab
itat
mod
elsbased
onqualitativedata(dep
enden
ton
statusof
spatiald
ata
detailo
fthe
mod
ela
vailab
ilityof
species
inform
ation)
gtYea
rto
multi-ye
ar
Speciesdataco
llection
may
nee
dto
occu
rov
ergt1ye
arto
provide
sufficien
tsamples
Month
sto
gtmulti-ye
armdash
Highly
dep
enden
ton
sourceof
datainput
(existingdata
new
empirical
data
collection
)
Statisticalm
odel
dev
elop
men
tan
drefinem
entcanbetime
consu
ming
Modelingap
proachmay
require
additional
wee
ksor
month
s
gtYea
rmdash
Forem
piricalspe
ciesndashh
abitat
mod
elsthat
requ
irenew
data
collection
Dep
enden
ton
timerequired
toco
mpile
evaluatean
dcatego
rize
hab
itat
andstressor
data
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Tab
le5
Exa
mple
ofaqualitativereview
(matrixtable)for
aproject-based
CEA
Valued
compon
ent
Potential
effects
Mitigation
compen
sation
Residual
effect
Sign
ifican
ceof
residual
effects
Eco
logical
societal
value
Ove
rallsign
ifican
ceMag
nitude
Spatiale
xten
t
Freq
uen
cy
probab
ility
Duration
Rev
ersibility
Graywolf
Assoc
iatedwith
major
preysp
ecies
(moo
sed
eer)A
t
closure
increa
sed
area
sof
grasslan
d
willc
reatewhite-
tailed
dee
rhab
itat
andpoten
tially
higher
dee
ran
d
wolfnumbers
Limitfootprintof
disturbed
area
s
Increase
indeeran
d
wolfp
opulation
s
over
timeResidual
adverseeffect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
highqualitymoo
se
hab
itattherefore
limited
wolf
pop
ulation
MIN
IMAL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
is
long-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
is
reversibledu
ring
site
reclam
ation
Moo
sefollow
edby
wolvessh
ould
return
tosite
at
curren
tlevels
LOW
Wolvesarecommon
inthelocaland
region
alstudy
areasLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
wolve
s
aremob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
American
black
bea
r
Initialm
igration
duringsite
preparationan
d
constru
ction
return
tosite
area
afterperiodof
human
noise
hab
ituation
Limitfootprintof
disturbed
areas
Establishwildlife
policyto
minim
ize
human
interaction
withwildlifean
d
decrease
poten
tial
forhab
ituation
Increa
sedseason
al
use
ofthesite
over
timedep
endson
succession
of
reclaimed
area
s
Residual
adve
rse
effect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
abov
eav
erag
ebea
r
hab
itatM
INIM
AL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
islong-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
reversibledu
ring
site
reclam
ation
New
grasslan
d
hab
itat
may
increase
the
season
alhab
itat
use
bybe
ars
LOW
Bearde
nsity
inthe
study
site
islikely
comparab
leto
that
inthesurrou
nding
landscapeLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
bea
rsare
mob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
NoteT
hetable
summarizes
theproposed
project
effectsmitigationan
dco
mpen
sation
mea
surespoten
tial
residual
effects(theen
vironmen
tale
ffectof
aproject
that
remainsaftermitigationmea
sureshav
ebee
nim
plemen
ted)sign
ificance
ofresidual
effectsin
considerationof
ava
rietyof
assessmen
tfactors(ratings
inboldface
type)a
ndwhether
asign
ificantad
verseen
vironmen
tale
ffecton
ava
lued
compon
entisex
pected
asaresu
ltof
project
implemen
tation
Sou
rceEnvironmen
talImpactStatem
entfortheMarathon
PGM-CuProject20
12Thetable
represents
description
san
dratings
summarizingtheen
vironmen
tale
ffects
ofametal
minedev
elop
men
ton
twova
lued
compon
ents
(graywolfan
dAmerican
black
bea
r)
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(Environmental Impact Statement for the Springbank Off-streamReservoir Project 2018) to estimate area changes in the amountof suitable habitat (based on habitat suitability models) for selectVCs Spraguersquos Pipit (Anthus spragueii) and Northern Leopard Frog(Lithobates pipiens) during different flood scenarios created by waterdiversion to a channel and reservoir within the proposed projectarea (Table 7)Effects-based CEAs typically involve the creation and application
of empirical speciesndashhabitat models within the study area Maceet al (1999) evaluated the CE of human and resource activity ongrizzly bear (Ursus arctos) habitat inwesternMontana during springsummer and fall seasons The authors modelled bear resourceselection from telemetry data satellite imagery elevation humanactivity points roads and trails and extrapolatedmodels to a largerregion with similar climate and vegetation Quantifying and map-ping areas with a high probability of use by bears in each seasonwas used to inform conservation and management actions (egroad or trail closures within important seasonal habitats habitatrestoration future road creation) Roumldder et al (2016) outline aninnovative approach to integrate SDM and potential connectivitymodels to identify connected and isolated populations and habitatcorridors for a species of conservation concern the Sand Lizard(Lacerta agilis) in western Germany (Fig 2) Quantifying potentialconnectivity areas (for movement like dispersal or migration) wasused to identify areas of strong connectivity persistent isolationand stable connective networks needed to connect populationsThis proposed framework may be particularly useful for proposeddevelopments that affect (i) portions of a previously larger inter-connected population or (ii) connective features between perma-nently colonized habitat patches Additional examples from thepublished literature are listed in Table 3
323 Advantages and limitations CEA questions and whenwhyto use3231 AdvantagesAdvantages of the habitat supply method include its ability to
quantify effects using loss of suitable habitat This approach
which has wide-spread application in wildlife research may besuitable when (i) data to map and measure habitat change areavailable but empirical data on species response to specific stres-sors are limited (ii) collaborative approaches focused on localissues and values are important (eg speciesndashhabitat associa-tions may be based on both qualitative (TK local and expertknowledge) and quantitative (speciesndashhabitat models) data) and(iii) data to develop empirical speciesndashhabitat models and adjusteddensity estimates are available so that total impacts can be com-pared to current and future population targets (eg Mahon et al2014)
3232 LimitationsLimitations to the habitat supply modelling approach include
the availability of accurate publicly available spatial data forhabitats and stressors and reliable specieshabitat associationsfrom the region of interest If additional data are required torefine existing habitat suitability models or create new empiricalspeciesndashhabitat models within a study area stakeholders or part-ners should a priori develop rigorous sampling designs and sur-vey protocols to meet project objectives We note two additionalcautions when using this approach Specieshabitat associationsbased on limited empirical data from within the region or datafrom outside the region require model validation and testing(Muir et al 2011) Finally stressor effects are limited to how stres-sors contribute to the loss of suitable habitat (habitat changesare used to infer population consequences) Habitat-based analy-sis alone may not be useful because (i) habitat is assumed to bea useful measure of population status (ii) habitat needs of the tar-get species or VC are assumed to be known (Schultz 2010) and(iii) specific stressor effects on the target species or VC are notquantified (eg the shape magnitude and direction of speciesrsquoresponse to stressors indirect or novel effects of stressors on speciesor interactions among stressors) Failure to identify which stressorsare influencing target species or VCsmay limit future research intobiological mechanisms (eg habitat selection movement disper-sal reproduction survival predatorndashprey dynamics) needed to
Fig 1 Example of a qualitative review (conceptual modeldiagram) for an effects-based CEA Figure represents an annual life cycle ofevents of Greater Sage-Grouse responding to oil and gas development in southwest Wyoming USA (from Heinrichs et al 2019 reproducedwith permission of Ecol Appl Vol 29 copy2019 Ecological Society of America)
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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
212 Environ Rev Vol 29 2021
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
Mahon and Pelech 217
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
218 Environ Rev Vol 29 2021
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
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Mahon and Pelech 221
Published by NRC Research Press
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iron
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Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
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Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
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Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
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Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
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Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
Millspaugh JJ and Thompson FR III 2009 Models for planning wildlifeconservation in large landscapes Elsevier Amsterdam the Netherlands
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Muir J Hawkes VC Tuttle KN and Mochizuk T 2011 Synthesis of habitatmodels used in the oil sands region LGL Report EA3259 Unpublished reportby LGL Limited Environmental Research Associates for the Cumulative Envi-ronmental Management Association (CEMA) mdash The Reclamation WorkingGroup (RWG) Fort McMurray Alta LGL Limited Sidney BC
Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
Noble B Liu J and Hackett P 2017 The contribution of project environmentalassessment to assessing and managing cumulative effects individually andcollectively insignificant Environ Manage 59 531ndash545 doi101007s00267-016-0799-7 PMID27885387
Nunavut Impact Review Board (NIRB) 2020 Proponents guide February 2020Report Available from httpswwwnirbca [last accessed 20 August 2020]
Parlee BL Geertsema K and Willier A 2012 Social-ecological thresholdsin a changing boreal landscape insights from Cree knowledge of the LesserSlave Lake Region of Alberta Ecol Soc 17 20 doi105751ES-04410-170220
Pearce J and Ferrier S 2000 Evaluating the predictive performance ofhabitat models developed using logistic regression Ecol Modell 133(3)225ndash245 doi101016S0304-3800(00)00322-7
Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
Salice CJ 2012 Multiple stressors and amphibians Contributions of adversehealth effects and altered hydroperiod to population decline and extinctionJ Herpetol 46(4) 675ndash681 doi10167011-091
Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
Sawyer H Kauffman MJ and Nielson RM 2009 Influence of well pad ac-tivity on winter habitat selection patterns of Mule Deer J Wildl Manage73(7) 1052ndash1061 doi1021932008-478
Schieck J Muhly T Huggard D Solymos P Pan D Heckbert S andBayne E 2014 Predicting the cumulative effects of human developmenton biodiversity in northeastern Alberta Petroleum Technology AllianceCanada (Alberta Upstream Petroleum Research Fund) Calgary AB Avail-able from httpsauprfptacorgecological-2a-tool-to-assess-cumulative-effects-of-development-on-biodiversity [last accessed 13 November 2018]
Schultz C 2010 Challenges in connecting cumulative effects analysis toeffective wildlife conservation planning BioScience 60(7) 545ndash551 doi101525bio201060710
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Mahon and Pelech 223
Published by NRC Research Press
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Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
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224 Environ Rev Vol 29 2021
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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtDSCReportingLevel 0PDFXOutputConditionIdentifier ()PDFXCompliantPDFOnly falseAllowTransparency falsePreserveCopyPage trueUsePrologue falseStartPage 1MonoImageDownsampleThreshold 10GrayImageDownsampleThreshold 10CheckCompliance [None]CreateJDFFile falsePDFXSetBleedBoxToMediaBox trueEmbedOpenType falseOPM 0PreserveOverprintSettings falseUCRandBGInfo RemoveColorImageDownsampleThreshold 10MonoImageDict ltltK -1gtgtGrayImageDownsampleType AverageDescription ltltENU (Use these settings to create Adobe PDF documents suitable for reliable viewing and printing of business documents Created PDF documents can be opened with Acrobat and Adobe Reader 50 and later)PTB 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ltFEFF4f7f752890194e9b8a2d7f6e5efa7acb7684002000410064006f006200650020005000440046002065874ef69069752865bc666e901a554652d965874ef6768467e5770b548c52175370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c4f86958b555f5df25efa7acb76840020005000440046002065874ef63002gtgtgtCropMonoImages trueDefaultRenderingIntent RelativeColorimetericPreserveHalftoneInfo falseColorImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtCropGrayImages truePDFXOutputCondition ()SubsetFonts trueEncodeMonoImages trueCropColorImages truePDFXNoTrimBoxError truegtgtsetdistillerparamsltltPageSize [61207920]HWResolution [600600]gtgtsetpagedevice
conditions (climate-induced vegetation change) and exposure tooil and gas stressors (Heinrichs et al 2019)
313 Advantages and limitations CEA questions and whenwhyto use
3131 AdvantagesThe advantage of qualitative review is its utility when quantita-
tive data on habitat and stressors and time and (or) financialresources are limited The approach also has the ability to (i) offera collaborative flexible and accessible approach when localissues and values are important (eg TK or combined TK and SKstudies) (ii) identify a wide range of stressors and mechanisms
(eg indirect effects species interactions stressors that cannotbe effectively mapped) and (iii) serve as a conceptual frameworkwithin a CEA using one or more quantitative methods to assessand (or) simulate multiple stressor effects (eg empirical speciesndashstressor models Bayesian belief network (BBN) models simulationmodels)
3132 LimitationsThe limitation of qualitative review is uncertainty surrounding
the objectivity and reliability of assessments particularly whenexpert opinion is applied to novel locations or stressors Specifi-cally these limitations include the inability to quantify any stres-sor related impacts including effects on habitat area (eg amount
Table 2 Cumulative effects assessment (CEA) methods identified in the stage one review (Canadian Impact Assessment Registry) and the stagetwo review (published reviews of CEAmethods and publicly available CEA guidance documents)
Stage CEAmethods
Stage 1 Canadian Impact Assessment Registry Total 2262 (35) with Environmental Impact Statement ImpactStatement Regional Assessment Report
Environmental Assessment under Canadian Environmental Assessment Act2012 (n = 50)
1650 (32) with Environmental Impact Statement1516 used QR416 used QR and HSM
Environmental Assessment by Review Panel under CanadianEnvironmental Assessment Act 2012 (n = 8)
58 (625) with Environmental Impact Statement55 used QR35 used QR and HSM15 used QR HSM and SM
Impact Assessment under Impact Assessment Act 2019 (n = 2) 02 (0) with Impact StatementRegional Assessment under Impact Assessment Act 2019 (n = 2) 12 (50) with Regional Assessment Report
1 used QR HSM and DSM
Stage 2 Reviews of CEAmethodsCocklin et al 1992 Environmental checklists matrix approaches inputndashoutputmethods
approaches including network analysisCanter and Atkinson 2011 Indicators and indices habitat suitability modelsDuinker et al 2013 GIS scenarios thresholds indicators and indices network analysis
matrices habitat suitability modelshabitat selection modelshabitat supply models stressor-based and effects-based approachesmultivariate statistical analysis air pollutionmodels landscapemodels animal population simulation models water-basedsimulationmodels public engagement community-basedmonitoring population viability analysis costndashbenefit analysisvisual amenity analysis traffic-light decision support systemsustainability appraisal
Hodgson and Halpern 2019 Experiment meta-analysis single species models mappingqualitative models multi-species models (aquaticmarine)
Marcot et al 2001 Marcot et al 2006 McCann et al 2006 Uusitalo 2007 Bayesian networkmodelsBayesian belief network (BBN) models
Stage 2 CEA guidance documentsTechnical Guidance for Assessing CEA under Canadian EnvironmentalAssessment Act 2012 Draft (CEAA 2014) Technical Guidance forAssessing CEA under Canadian Environmental Assessment Act 2012InterimGuidance (CEAA 2018)
Questionnaires and interviews checklists and matrices network andsystems analysisdiagrams indicators and indices conceptual andnumerical models trend analysis spatial analysis
Section 22-Factors to be considered (IAAC 2019 httpswwwcanadacaenimpact-assessment-agency)
New IAAC guidance in preparation interim guidance is CEAA (2018)
Consideration of cumulative effects in YESAB Assessments (YESAB 20052019)
No CEAmethods
Environmental Assessment Guidelines (MVEIRB 2004) No CEAmethodsProponents Guide NIRB Technical Guide Series (NIRB 2020) No CEAmethods
Note Agency definitions CEAA Canadian Environmental Assessment Agency IACC Impact Assessment Agency of Canada administers projects under CanadianEnvironmental Assessment Act 2012 and Impact Assessment Act 2019 YESAB Yukon Environmental and Socio-economic Assessment Board administers projects underYukon Environmental and Socio-economic Assessment Act 2003 MVEIRB Mackenzie Valley Environmental Impact Review Board administers projects underMackenzie ValleyResource Management Act 1998 Nunavut Impact Review Board (NIRB) administers projects under Nunavut Planning and Project Assessment Act 2013 CEA methoddefinitions QR qualitative review HSM habitat supply models SM simulation models DSM decision support models
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of suitable habitat lost) and direct (eg habitat loss mortality) orindirect stressor effects on species (eg altered habitat selectionreduced demographic rates like reproduction or survival)
3133 Science-based CEA questionsSee Table 4 to identify how qualitative review methods address
key science-based CEA questions
3134 Whenwhy to usemdash project-based CEAsldquoWhen to userdquo when quantitative data on species and spatial
data on habitats or stressors limit the development of speciesndashhabitatmodels (to assess change in habitat) and (or) speciesndashhabitatndashstressor models (to assess species response to habitat and stressors)Qualitative data and qualitative reviewmethods can be used to iden-tify rank and categorize stressor effects ldquoWhy to userdquo allows for acomprehensive listing and ranking of potential stressor effects onVCs in relation to (i) mitigation measures for the proposed projectand (ii) other past current or future projects
3135 Whenwhy to usemdash effects-based CEAsldquoWhen to userdquo when systems are complex conceptual diagrams
can be combined with other quantitative methods and used to(i) identify multiple stressors and (or) pathways of effects on dif-ferent life stages of target species or VCs (see Fisher and Burton2018 Pirotta et al 2018 and Heinrichs et al 2019 for examples)and (ii) outline the analysis process or framework (see Munns2006 and Katzner et al 2020 for examples) ldquoWhy to userdquo allowsfor a visual representation of the system (stressors ecologicaldrivers individual and population responses) and the analysisprocess
32 Habitat supply models
321 Description of methodIn this category we include various forms of speciesndashhabitat
modelling currently the most common approach to terrestrialspecies assessment in most project-based CEAs as well as in con-servation planning and landscape management (eg Morrisonet al 2006) Approaches vary in complexity from simple literature-based habitat rankings to complex statistical models of empirical
speciesndashhabitat relationships (see Table 3) A defining characteristicof this method for CEA is that habitat is used as a surrogate for theidentified terrestrial species (or as a VC itself) For habitat supplymodels quantitative changes in the amount and (or) quality of aspeciesrsquo habitat (typically categorized and measured within a GIS)are used as the primary measure of CE In contrast for empiricalspeciesndashstressormodels the species direct response to one ormorestressors is used as the primary measure of CE We provide morescientific background to this approach subsequently however ingeneral this approachwith respect to CEA typically involves
1 Developing speciesndashhabitat associations or models Thisrequires ranking the suitabilityeffectivenessfunctionality ofmeasurable habitat categories for target species using expertopinion literature review and (or) empirical speciesndashhabitatcorrelations
2 Quantifying changes in the amount of this habitat as a resultof the proposed project or projects and existingfuture stressors
3 Inferring effects on species as a result of these habitat changes
Speciesndashhabitat models can be developed using a variety ofexpert opinion and statistical modelling approaches have a longhistory in wildlife management and landscape planning and arewell-documented in the literature (eg Morrison et al 2006Millspaugh and Thompson 2009 Guisan et al 2013) Standardsfor developing literature and expert opinion based habitat suit-ability indices (HSI) date back to the 1980s (US Fish and WildlifeService (USFWS) 1980 1981) and many jurisdictions have simi-larly developed protocols for wildlife habitat rankings in Canada(BC Resource Inventory Committee 1999 Clarke 2012) Thesehabitat suitability models typically use experts and (or) compilespecies information from existing literature to rank habitats fora species (eg on a 0ndash1 scale or similar categorical ranking sys-tem) Additional variables that may modify habitat quality (egproximity to roads) are also often considered to differentiatebetween habitat capability (the potential of the habitat to sup-port the species under optimal conditions) and habitat suitabilityor effectiveness (the actual ability of the habitat to support the spe-cies in a specific study area)
Table 3 Cumulative effects assessment (CEA) methods identified in the stage three review (targeted reviews of CEA methods) qualitativereview habitat supply models empirical speciesndashstressor models and decision support models (Bayesian belief network (BBN) models)
Stage 3 Targeted reviews of CEAmethods References
Qualitative review Chilima et al 2013 (Ontario CAN watersheds) Cooper 2011 (United Kingdom multiple species)Swor and Canter 2011 (Pennsylvania USA water fish mussels riparianfloodplain resources)
Habitat supply models Canter and Atkinson 2011 (USA multiple species) Houle et al 2010 (Quebec CAN gray wolf)Johnson et al 2005 (Northwest Territories CAN caribou gray wolf grizzly bear wolverine) Maceet al 1999 (Montana USA grizzly bear) Roumldder et al 2016 (Cologne Germany Sand Lizard)
Empirical species-stressor models Sawyer et al 2009 (Wyoming USA mule deer) Houle et al 2010 (Quebec CAN gray wolf) Salice et al2011 (South Carolina USA Eastern Narrow-mouthed Toad) Salice 2012 (southeast USA EasternNarrow-mouthed Toad) Wilson et al 2013 (northwest Alaska USA caribou) Goodale andMilman2019 (offshore regions waterfowl waterbirds landbirds) Hodgson et al 2017 (various locationsinsects birds fish ungulates) Jones et al 2017 (Muskoka RiverWatershed CAN benthic invertebrates)Fisher and Burton 2018 (northeast Alberta CAN gray wolf white-tailed deermoose American blackbear coyote Canada lynx fisher red fox snowshoe hare American red squirrel) Claireau et al 2019(western France 10 bat species) Daniel and Koper 2019 (southern Alberta CAN 5 grassland landbirdspecies) Heinrichs et al 2019 (Wyoming USA Greater Sage-Grouse) Mahon et al 2019 (northeastAlberta CAN 27 boreal landbird species) Katzner et al 2020 (California USA roadrunner hawk)Leston et al 2020 (northeast Alberta CAN 20 boreal landbird species)
McNay et al 2006 (north-central British Columbia CAN caribou) Steventon et al 2006 (north coastof British Columbia CAN Marbled Murrelet) Howes et al 2010 (eastern Australia woodlandlandbirds) MacCracken et al 2013 (Bering and Chukchi Seas Pacific walrus) Ban et al 2014 (GreatBarrier Reef Australia reef coral) Fortin et al 2016 (Alaska USA brown bears) Mantyka-Pringleet al 2016 (South East Queensland Australia macroinvertebrates fish) Mantyka-Pringle et al 2017(Slave River Delta Northwest Territories CAN fish waterfowl mammals)
206 Environ Rev Vol 29 2021
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r pe
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Tab
le4
Summaryco
mparison
ofan
alytical
method
sforcu
mulative
effectsassessmen
t(CEA)for
terrestrialspecies
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Cumulative
effectsof
stressors
bas
edon
Exp
ertknow
ledge
literature
review
oflikelyeffects
Estim
ated
chan
gein
amou
ntof
suitab
lehab
itat
forsp
eciesas
aresu
ltof
stressorssu
itab
lehab
itat
isch
arac
terize
dusingex
pert
opinionliterature
and(or)
empirical
data
Directmea
suremen
tsof
speciesrsquo
resp
onse
tograd
ientof
stressors
andco
mbinationsof
stressors
Mod
eled
respon
seof
speciesab
unda
nce
orhab
itat
tocombined
hab
itatstressor
andother
inpu
ts(in
putsmay
bequ
alitativeor
empiricald
ata)
Most
commonly
asso
ciated
with
Project-based
CEA
Project-based
CEAscien
tific
research
Effects-based
CEAscien
tific
research
Effects-based
CEAscien
tificresearch
Key
strength
s
Limited
data
time
andco
strequirem
ents
Welle
stab
lish
edap
proach
Addresses
allo
rmostCEAgo
als
question
susingdirectmea
sures
ofsp
ecies
stressor
relation
ship
Can
incorporatemultipleda
tatype
san
dsources(qua
litativean
dqu
antitative
data)
Can
includeawiderange
ofstressors
Allow
squan
tification
ofeffects
(egloss
ofsu
itab
lehab
itat)w
hen
directsp
eciesndashstressor
resp
onse
orsp
eciesdataarelimited
Other
strengthslimitationsba
sedon
theex
tentto
whichqu
alitativeversus
quan
titative
dataareused
Key
limitations
Fa
ilsto
answ
erCEAquestion
sor
doe
sso
withuncertainreliab
ility
Inco
rporates
effectson
lydueto
hab
itat
loss
Highdata
timean
dco
strequirem
ents
Infreq
uen
tuse
interrestrialsystems
may
limitap
plication
Accuracy
andreliab
ilityhighly
dep
enden
ton
qualityan
dprecision
ofsp
ecies
hab
itat
associations
Restrictedto
speciesthat
can
beread
ilysampled
Fa
ilsto
address
man
yCEA
objectives
(interactionssh
ape
ofsp
eciesndashstressor
resp
onse)
Quan
titative
datarequirem
ents
Spec
ies
Nonemdash
Not
quan
titative
Lo
wmdash
Ifbased
onqualitative
species
hab
itat
associations
Veryhighmdash
Req
uires
species
metrics
across
fullgrad
ientof
stressors(andco
mbinationsif
testingforinteractions)
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareusedin
themod
el
Highmdash
Ifbased
onem
pirical
data
species
hab
itat
associations
(see
text)
Hab
itat
mdashsp
atiald
ata
Nonemdash
Not
quan
titative
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Stressormdash
spatiald
ata
Nonemdash
Not
quan
titative
Highto
very
highmdash
Tobe
effectiverequires
accu
rate
accessible
spatiald
ataon
ldquoindividualrdquostressorsoftenbroad
sector
catego
ries
(mining
oilg
as)
areused
Veryhighmdash
Tobeeffective
requires
accu
ratea
ccessible
spatiald
ataon
ldquoindividualrdquo
stressors
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Mahon and Pelech 207
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Tab
le4(continu
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
answ
erkey
CEAquestionsan
dap
plica
tionofCEA
What
isth
erelative
mag
nitudean
ddirection
ofindividual
stressor
effects
Xmdash
Only
based
onqualitativedata
Based
onrelative
contribution
ofstressor
toreducingam
ountof
estimated
suitab
lehab
itat
(req
uires
ldquobackfillingrdquo
individual
stressorsin
GISsee
text)
Pa
rameter
estimates
inmod
elrepresentmag
nitudedirection
and
certainty
ofstressor
effects
Stressor
effectstypically
are
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Relativeco
ntribution
ofstressors
toloss
ofsu
itab
lehab
itat
canbe
analysed
(req
uires
ldquoback-fillingrdquo
individual
stressorsin
GISsee
text)
What
isth
esh
apeofth
esp
eciesndashstressorresp
onse
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edNon
linea
rrelation
ships
(polyn
omial)canbetested
via
mod
elselectionan
dread
ily
grap
hed
Sh
apeof
theresp
onse
canbe
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Dostressors
combin
ead
ditivelyorin
tera
ctively
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edInteractionterm
scanbetested
forinclusion
viamod
elselection
procedures
Interactioneffectscanbeestimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rceof
inputmay
bequalitativeinform
ationor
empirical
mod
els)
Coe
fficien
tsgraphscanbeusedto
catego
rize
synergisticve
rsus
antago
nisticeffects
What
isth
eove
rallim
pac
tofcu
mulative
effectson
populations
Xmdash
Only
based
onqualitative
data
Based
onloss
ofsu
itab
lehab
itat
Sp
eciesndashhab
itatndashstressor
mod
els
canbeusedto
predictan
dco
mpare
speciesab
undan
cemea
sures
amon
gldquon
o-disturban
cereferen
cerdquo
curren
tan
dfuture
landscap
eco
nditionsbased
onGISla
ndscap
esimulation
Po
pulation
resp
onseoutputcanbe
compared
amon
gldquon
o-disturban
ce
referencerdquocu
rren
tan
dfuture
scen
arioswithmod
elinput(hab
itat
stressor
amou
nts)b
ased
onGIS
landscap
esimulation
Sp
eciesndashhab
itat
mod
elprediction
sforhab
itat
amou
nts
orsp
ecies
abunda
nce
canbe
compa
redam
ong
ldquono-disturban
cereferen
cerdquocu
rren
tan
dfuture
landscapecondition
sba
sedon
GISla
ndscapesimulation
What
isth
eove
rallim
pac
tonth
eco
mmunity
Xmdash
Only
based
onqualitative
data
Req
uires
calculatingspeciesndashhab
itat
mod
elsforallspeciescalculating
predicted
abunda
ncesin
reference
curren
tor
future
landscapesand
comparingcommunitymetrics
amon
gthesepredictedcommun
ities
Withsu
fficien
tdata
community
metrics
canbeco
mpared
amon
glowh
ighstressor
catego
ries
Req
uires
calculatingpop
ulation
resp
onsesforsp
eciesin
reference
curren
tor
future
landscap
esa
nd
comparingco
mmunitymetrics
amon
gthesepredictedco
mmunities
Com
munitymetrics
cansimilarly
becompa
redusingspeciesabun
dance
inreference
curren
tor
future
landscap
esa
spredictedfrom
empiricalspeciesndashhab
itatndashstressor
mod
els
How
certainar
eresu
lts
How
cance
rtainty
be
assessed
Verylow
mdashSu
bjectivenature
mak
esitdifficu
ltto
quan
tify
certainty
Verylowmdash
Ifspeciesndashhab
itat
basedon
qualitativeinform
ation(subjective
nature
mak
esitdifficu
ltto
quan
tify
certainty)
Highmdash
Statisticalsignificance
ofstressor
effectscanbeestimated
viaco
nfiden
ceintervalsof
param
eter
estimates
Moderateto
highmdash
Dep
endson
extent
towhichmod
elsareba
sedon
qualitativeversusem
piricalda
ta
Mod
eratemdash
Ifspecies
habitat
relation
shipsbasedon
empiricald
ata
Approachallowsuncertainty
ofrelation
shipsto
bequan
tified
and
inputinto
themod
els
Ran
geofstressors
evaluated
Highmdash
Not
limited
bysp
atial
dataon
stressors
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
andaccessible
spatiald
ata
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
and
accessible
spatiald
ata
Highmdash
Not
necessarily
limited
by
spatiald
ataon
stressorsifmod
els
based
onqualitativerelation
ships
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In more recent and complex approaches empirical speciesndashhabitat models are developed by sampling terrestrial speciesacross habitat types in a study area and using advanced statisticalanalyses to quantify species associations with different habitatsModels are termed resource selection functions (RSF) when spe-cies data (presenceabsence usedunused) are analyzed at smallwithin-population scales and species distribution models (SDM)or species abundance models (SAM) when data (occurrence abun-dance respectively) are analyzed at large range-level scales (Guisanand Zimmermann 2000 Guisan and Thuiller 2005 Elith andLeathwick 2009 Hegel et al 2010) For terrestrial birds current andemerging methods can be followed to convert species counts at asample station (the most common sample metric for terrestrialbirds) to a density estimate (Soacutelymos et al 2013 Matsuoka et al2014) Reporting species responses as densities (versus relativeabundance) provides (i) a relevant and readily communicatedmeasure of species response (ii) a standardized measure acrossregions and studies and (iii) an evaluation against conservationor management targets or population viability estimatesA range of statistical modeling frameworks are available to analyse
speciesndashhabitat relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) Typically habitat coefficients in these modelsare used to assign habitat rankings for species Both expert andempirically based habitat rankings require validation againstexternal data (or internal validations in the case of empiricalmodels) to ensure their reliability for an area (Pearce and Ferrier2000 Boyce et al 2002 Muir et al 2011) Once validated modelresults are used to assess the amount and distribution of suitablehabitat for species within a GIS (Millspaugh and Thompson 2009)Additionally when species responses have been converted to den-sity estimates coefficients can be coupled with GIS maps to esti-mate species densities or population sizes Within the context ofCEA the footprint or area of proposed projects existing projectsand disturbances and future land use scenarios can be overlaidon these maps to estimate the change in suitable habitat andassess effects on terrestrial species based on these habitat changeswithin the study area
322 Examples of applicationsWe highlight both project-based CEA and effects-based CEA
examples to demonstrate the flexibility of the habitat supplymethod Project-based CEAs typically use literature-based or hab-itat suitability models to identify map and quantify the area ofsuitable habitat for a given species within the study area We pro-vide a hypothetical example to explain how the habitat supplymethod is typically used to assess project-specific effects (Table 6)Changes in the amount of suitable habitat during the project(application phase eg from 10 to 6 of study area in Table 6)and after the project (closureremediation phase eg 0 changein Table 6) are estimated relative to current or baseline The hab-itat supply method is not typically used to assess CE because theextent to which existing or past stressors have affected pre-existinghabitat types is often not known Instead CE requirements areoften met by comparing the total area of the proposed project (eg5 of study area in Table 6) to the total area of other stressors regard-less of habitat affected (eg 35 Table 6) Thus in high or moder-ately disturbed areas these comparisons may suggest that a projectwould have minimal added effects relative to existing stressorsWhere data and resources allow some project-based CEAs mayuse historic maps or create hypothetical no-disturbance land-scapes within a GIS (eg see methods in ABMI 2017 Mahon et al2019) to assess how other stressors may have affected specific habi-tats However resource requirements (time data technical exper-tise) place this type of assessment beyond themandate or capacity ofmost project-based CEAs We include an example of the habitat sup-ply method from a recent project-based CEA from the IAAC RegistryT
able
4(conclud
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
inco
rpora
tetrad
itional
knowledge
Yes
Notusu
ally
Notusu
ally
Yes
Statistica
ltech
nical
expertise
required
Low
tomoderatemdash
Mod
eratewith
form
almod
eling
Moderate
High
Highto
very
high(specialized)
Tim
efra
me
Month
s
Month
sto
ltyea
rmdash
For
speciesndashhab
itat
mod
elsbased
onqualitativedata(dep
enden
ton
statusof
spatiald
ata
detailo
fthe
mod
ela
vailab
ilityof
species
inform
ation)
gtYea
rto
multi-ye
ar
Speciesdataco
llection
may
nee
dto
occu
rov
ergt1ye
arto
provide
sufficien
tsamples
Month
sto
gtmulti-ye
armdash
Highly
dep
enden
ton
sourceof
datainput
(existingdata
new
empirical
data
collection
)
Statisticalm
odel
dev
elop
men
tan
drefinem
entcanbetime
consu
ming
Modelingap
proachmay
require
additional
wee
ksor
month
s
gtYea
rmdash
Forem
piricalspe
ciesndashh
abitat
mod
elsthat
requ
irenew
data
collection
Dep
enden
ton
timerequired
toco
mpile
evaluatean
dcatego
rize
hab
itat
andstressor
data
Mahon and Pelech 209
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r pe
rson
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se o
nly
Tab
le5
Exa
mple
ofaqualitativereview
(matrixtable)for
aproject-based
CEA
Valued
compon
ent
Potential
effects
Mitigation
compen
sation
Residual
effect
Sign
ifican
ceof
residual
effects
Eco
logical
societal
value
Ove
rallsign
ifican
ceMag
nitude
Spatiale
xten
t
Freq
uen
cy
probab
ility
Duration
Rev
ersibility
Graywolf
Assoc
iatedwith
major
preysp
ecies
(moo
sed
eer)A
t
closure
increa
sed
area
sof
grasslan
d
willc
reatewhite-
tailed
dee
rhab
itat
andpoten
tially
higher
dee
ran
d
wolfnumbers
Limitfootprintof
disturbed
area
s
Increase
indeeran
d
wolfp
opulation
s
over
timeResidual
adverseeffect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
highqualitymoo
se
hab
itattherefore
limited
wolf
pop
ulation
MIN
IMAL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
is
long-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
is
reversibledu
ring
site
reclam
ation
Moo
sefollow
edby
wolvessh
ould
return
tosite
at
curren
tlevels
LOW
Wolvesarecommon
inthelocaland
region
alstudy
areasLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
wolve
s
aremob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
American
black
bea
r
Initialm
igration
duringsite
preparationan
d
constru
ction
return
tosite
area
afterperiodof
human
noise
hab
ituation
Limitfootprintof
disturbed
areas
Establishwildlife
policyto
minim
ize
human
interaction
withwildlifean
d
decrease
poten
tial
forhab
ituation
Increa
sedseason
al
use
ofthesite
over
timedep
endson
succession
of
reclaimed
area
s
Residual
adve
rse
effect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
abov
eav
erag
ebea
r
hab
itatM
INIM
AL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
islong-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
reversibledu
ring
site
reclam
ation
New
grasslan
d
hab
itat
may
increase
the
season
alhab
itat
use
bybe
ars
LOW
Bearde
nsity
inthe
study
site
islikely
comparab
leto
that
inthesurrou
nding
landscapeLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
bea
rsare
mob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
NoteT
hetable
summarizes
theproposed
project
effectsmitigationan
dco
mpen
sation
mea
surespoten
tial
residual
effects(theen
vironmen
tale
ffectof
aproject
that
remainsaftermitigationmea
sureshav
ebee
nim
plemen
ted)sign
ificance
ofresidual
effectsin
considerationof
ava
rietyof
assessmen
tfactors(ratings
inboldface
type)a
ndwhether
asign
ificantad
verseen
vironmen
tale
ffecton
ava
lued
compon
entisex
pected
asaresu
ltof
project
implemen
tation
Sou
rceEnvironmen
talImpactStatem
entfortheMarathon
PGM-CuProject20
12Thetable
represents
description
san
dratings
summarizingtheen
vironmen
tale
ffects
ofametal
minedev
elop
men
ton
twova
lued
compon
ents
(graywolfan
dAmerican
black
bea
r)
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(Environmental Impact Statement for the Springbank Off-streamReservoir Project 2018) to estimate area changes in the amountof suitable habitat (based on habitat suitability models) for selectVCs Spraguersquos Pipit (Anthus spragueii) and Northern Leopard Frog(Lithobates pipiens) during different flood scenarios created by waterdiversion to a channel and reservoir within the proposed projectarea (Table 7)Effects-based CEAs typically involve the creation and application
of empirical speciesndashhabitat models within the study area Maceet al (1999) evaluated the CE of human and resource activity ongrizzly bear (Ursus arctos) habitat inwesternMontana during springsummer and fall seasons The authors modelled bear resourceselection from telemetry data satellite imagery elevation humanactivity points roads and trails and extrapolatedmodels to a largerregion with similar climate and vegetation Quantifying and map-ping areas with a high probability of use by bears in each seasonwas used to inform conservation and management actions (egroad or trail closures within important seasonal habitats habitatrestoration future road creation) Roumldder et al (2016) outline aninnovative approach to integrate SDM and potential connectivitymodels to identify connected and isolated populations and habitatcorridors for a species of conservation concern the Sand Lizard(Lacerta agilis) in western Germany (Fig 2) Quantifying potentialconnectivity areas (for movement like dispersal or migration) wasused to identify areas of strong connectivity persistent isolationand stable connective networks needed to connect populationsThis proposed framework may be particularly useful for proposeddevelopments that affect (i) portions of a previously larger inter-connected population or (ii) connective features between perma-nently colonized habitat patches Additional examples from thepublished literature are listed in Table 3
323 Advantages and limitations CEA questions and whenwhyto use3231 AdvantagesAdvantages of the habitat supply method include its ability to
quantify effects using loss of suitable habitat This approach
which has wide-spread application in wildlife research may besuitable when (i) data to map and measure habitat change areavailable but empirical data on species response to specific stres-sors are limited (ii) collaborative approaches focused on localissues and values are important (eg speciesndashhabitat associa-tions may be based on both qualitative (TK local and expertknowledge) and quantitative (speciesndashhabitat models) data) and(iii) data to develop empirical speciesndashhabitat models and adjusteddensity estimates are available so that total impacts can be com-pared to current and future population targets (eg Mahon et al2014)
3232 LimitationsLimitations to the habitat supply modelling approach include
the availability of accurate publicly available spatial data forhabitats and stressors and reliable specieshabitat associationsfrom the region of interest If additional data are required torefine existing habitat suitability models or create new empiricalspeciesndashhabitat models within a study area stakeholders or part-ners should a priori develop rigorous sampling designs and sur-vey protocols to meet project objectives We note two additionalcautions when using this approach Specieshabitat associationsbased on limited empirical data from within the region or datafrom outside the region require model validation and testing(Muir et al 2011) Finally stressor effects are limited to how stres-sors contribute to the loss of suitable habitat (habitat changesare used to infer population consequences) Habitat-based analy-sis alone may not be useful because (i) habitat is assumed to bea useful measure of population status (ii) habitat needs of the tar-get species or VC are assumed to be known (Schultz 2010) and(iii) specific stressor effects on the target species or VC are notquantified (eg the shape magnitude and direction of speciesrsquoresponse to stressors indirect or novel effects of stressors on speciesor interactions among stressors) Failure to identify which stressorsare influencing target species or VCsmay limit future research intobiological mechanisms (eg habitat selection movement disper-sal reproduction survival predatorndashprey dynamics) needed to
Fig 1 Example of a qualitative review (conceptual modeldiagram) for an effects-based CEA Figure represents an annual life cycle ofevents of Greater Sage-Grouse responding to oil and gas development in southwest Wyoming USA (from Heinrichs et al 2019 reproducedwith permission of Ecol Appl Vol 29 copy2019 Ecological Society of America)
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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
218 Environ Rev Vol 29 2021
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al u
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
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Austin M 2007 Species distribution models and ecological theory a criticalassessment and some possible new approaches Ecol Modell 200 1ndash19 doi101016jecolmodel200607005
Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
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Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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iron
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ded
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om b
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AT
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AC
IFIC
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NA
TL
LA
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n 12
07
21Fo
r pe
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al u
se o
nly
Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
Canadian Environmental Assessment Agency (CEAA) 2015 Assessing Cumula-tive Environmental Effects under the Canadian Environmental AssessmentAct 2012 March 2015 Report Available from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
Canadian Environmental Assessment Agency (CEAA) Expert Panel Review 2017Building common ground a new vision for impact assessment in Canada Thefinal report of the expert panel for the review of Environmental assessmentprocesses Available from httpswwwcanadacaenservicesenvironmentconservationassessmentsenvironmental-reviewsenvironmental-assessment-processesbuilding-common-groundhtml [last accessed 10 April 2020]
Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
Environmental Impact Statement for the Springbank Off-stream ReservoirProject 2018 Environmental Impact Assessment of the Springbank Off-stream Reservoir Project Stantec Incorporated Available from httpsiaac-aeicgcca [last accessed 15 June 2020]
Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
Impact Assessment Agency of Canada (IAAC) 2019 The Impact AssessmentProcess Timelines and Outputs Available from httpswwwcanadacaenimpact-assessment-agencyhtml [last accessed 20 August 2020]
Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
Johnson C 2016 Understanding the spatial responses of caribou to human-caused disturbance In Development of modeling tools to address cumulativeeffects on the summer range of the Bathurst caribou herd ndash a demonstrationproject Edited by JS Nishi and A Gunn Environment and Natural ResourcesGovernment of the Northwest Territories Yellowknife NT pp 13ndash36
Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
Noble B Liu J and Hackett P 2017 The contribution of project environmentalassessment to assessing and managing cumulative effects individually andcollectively insignificant Environ Manage 59 531ndash545 doi101007s00267-016-0799-7 PMID27885387
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Pearce J and Ferrier S 2000 Evaluating the predictive performance ofhabitat models developed using logistic regression Ecol Modell 133(3)225ndash245 doi101016S0304-3800(00)00322-7
Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
Salice CJ 2012 Multiple stressors and amphibians Contributions of adversehealth effects and altered hydroperiod to population decline and extinctionJ Herpetol 46(4) 675ndash681 doi10167011-091
Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
Sawyer H Kauffman MJ and Nielson RM 2009 Influence of well pad ac-tivity on winter habitat selection patterns of Mule Deer J Wildl Manage73(7) 1052ndash1061 doi1021932008-478
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Mahon and Pelech 223
Published by NRC Research Press
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Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
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Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
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224 Environ Rev Vol 29 2021
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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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 ltFEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020be44c988b2c8c2a40020bb38c11cb97c0020c548c815c801c73cb85c0020bcf4ace00020c778c1c4d558b2940020b3700020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002egtNLD (Gebruik deze instellingen om Adobe PDF-documenten te maken waarmee zakelijke documenten betrouwbaar kunnen worden weergegeven en afgedrukt De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 50 en hoger)NOR 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 ltFEFF0041006e007600e4006e00640020006400650020006800e4007200200069006e0073007400e4006c006c006e0069006e006700610072006e00610020006f006d002000640075002000760069006c006c00200073006b006100700061002000410064006f006200650020005000440046002d0064006f006b0075006d0065006e007400200073006f006d00200070006100730073006100720020006600f60072002000740069006c006c006600f60072006c00690074006c006900670020007600690073006e0069006e00670020006f006300680020007500740073006b007200690066007400650072002000610076002000610066006600e4007200730064006f006b0075006d0065006e0074002e002000200053006b006100700061006400650020005000440046002d0064006f006b0075006d0065006e00740020006b0061006e002000f600700070006e00610073002000690020004100630072006f0062006100740020006f00630068002000410064006f00620065002000520065006100640065007200200035002e00300020006f00630068002000730065006e006100720065002egtDAN 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 (Utilizzare queste impostazioni per creare documenti Adobe PDF adatti per visualizzare e stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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 ltFEFF4f7f75288fd94e9b8bbe5b9a521b5efa7684002000410064006f006200650020005000440046002065876863900275284e8e55464e1a65876863768467e5770b548c62535370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c676562535f00521b5efa768400200050004400460020658768633002gtSUO 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 ltFEFF005500740069006c0069006300650020006500730074006100200063006f006e0066006900670075007200610063006900f3006e0020007000610072006100200063007200650061007200200064006f00630075006d0065006e0074006f0073002000640065002000410064006f00620065002000500044004600200061006400650063007500610064006f007300200070006100720061002000760069007300750061006c0069007a00610063006900f3006e0020006500200069006d0070007200650073006900f3006e00200064006500200063006f006e006600690061006e007a006100200064006500200064006f00630075006d0065006e0074006f007300200063006f006d00650072006300690061006c00650073002e002000530065002000700075006500640065006e00200061006200720069007200200064006f00630075006d0065006e0074006f00730020005000440046002000630072006500610064006f007300200063006f006e0020004100630072006f006200610074002c002000410064006f00620065002000520065006100640065007200200035002e003000200079002000760065007200730069006f006e0065007300200070006f00730074006500720069006f007200650073002egtCHT ltFEFF4f7f752890194e9b8a2d7f6e5efa7acb7684002000410064006f006200650020005000440046002065874ef69069752865bc666e901a554652d965874ef6768467e5770b548c52175370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c4f86958b555f5df25efa7acb76840020005000440046002065874ef63002gtgtgtCropMonoImages trueDefaultRenderingIntent RelativeColorimetericPreserveHalftoneInfo falseColorImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtCropGrayImages truePDFXOutputCondition ()SubsetFonts trueEncodeMonoImages trueCropColorImages truePDFXNoTrimBoxError truegtgtsetdistillerparamsltltPageSize [61207920]HWResolution [600600]gtgtsetpagedevice
of suitable habitat lost) and direct (eg habitat loss mortality) orindirect stressor effects on species (eg altered habitat selectionreduced demographic rates like reproduction or survival)
3133 Science-based CEA questionsSee Table 4 to identify how qualitative review methods address
key science-based CEA questions
3134 Whenwhy to usemdash project-based CEAsldquoWhen to userdquo when quantitative data on species and spatial
data on habitats or stressors limit the development of speciesndashhabitatmodels (to assess change in habitat) and (or) speciesndashhabitatndashstressor models (to assess species response to habitat and stressors)Qualitative data and qualitative reviewmethods can be used to iden-tify rank and categorize stressor effects ldquoWhy to userdquo allows for acomprehensive listing and ranking of potential stressor effects onVCs in relation to (i) mitigation measures for the proposed projectand (ii) other past current or future projects
3135 Whenwhy to usemdash effects-based CEAsldquoWhen to userdquo when systems are complex conceptual diagrams
can be combined with other quantitative methods and used to(i) identify multiple stressors and (or) pathways of effects on dif-ferent life stages of target species or VCs (see Fisher and Burton2018 Pirotta et al 2018 and Heinrichs et al 2019 for examples)and (ii) outline the analysis process or framework (see Munns2006 and Katzner et al 2020 for examples) ldquoWhy to userdquo allowsfor a visual representation of the system (stressors ecologicaldrivers individual and population responses) and the analysisprocess
32 Habitat supply models
321 Description of methodIn this category we include various forms of speciesndashhabitat
modelling currently the most common approach to terrestrialspecies assessment in most project-based CEAs as well as in con-servation planning and landscape management (eg Morrisonet al 2006) Approaches vary in complexity from simple literature-based habitat rankings to complex statistical models of empirical
speciesndashhabitat relationships (see Table 3) A defining characteristicof this method for CEA is that habitat is used as a surrogate for theidentified terrestrial species (or as a VC itself) For habitat supplymodels quantitative changes in the amount and (or) quality of aspeciesrsquo habitat (typically categorized and measured within a GIS)are used as the primary measure of CE In contrast for empiricalspeciesndashstressormodels the species direct response to one ormorestressors is used as the primary measure of CE We provide morescientific background to this approach subsequently however ingeneral this approachwith respect to CEA typically involves
1 Developing speciesndashhabitat associations or models Thisrequires ranking the suitabilityeffectivenessfunctionality ofmeasurable habitat categories for target species using expertopinion literature review and (or) empirical speciesndashhabitatcorrelations
2 Quantifying changes in the amount of this habitat as a resultof the proposed project or projects and existingfuture stressors
3 Inferring effects on species as a result of these habitat changes
Speciesndashhabitat models can be developed using a variety ofexpert opinion and statistical modelling approaches have a longhistory in wildlife management and landscape planning and arewell-documented in the literature (eg Morrison et al 2006Millspaugh and Thompson 2009 Guisan et al 2013) Standardsfor developing literature and expert opinion based habitat suit-ability indices (HSI) date back to the 1980s (US Fish and WildlifeService (USFWS) 1980 1981) and many jurisdictions have simi-larly developed protocols for wildlife habitat rankings in Canada(BC Resource Inventory Committee 1999 Clarke 2012) Thesehabitat suitability models typically use experts and (or) compilespecies information from existing literature to rank habitats fora species (eg on a 0ndash1 scale or similar categorical ranking sys-tem) Additional variables that may modify habitat quality (egproximity to roads) are also often considered to differentiatebetween habitat capability (the potential of the habitat to sup-port the species under optimal conditions) and habitat suitabilityor effectiveness (the actual ability of the habitat to support the spe-cies in a specific study area)
Table 3 Cumulative effects assessment (CEA) methods identified in the stage three review (targeted reviews of CEA methods) qualitativereview habitat supply models empirical speciesndashstressor models and decision support models (Bayesian belief network (BBN) models)
Stage 3 Targeted reviews of CEAmethods References
Qualitative review Chilima et al 2013 (Ontario CAN watersheds) Cooper 2011 (United Kingdom multiple species)Swor and Canter 2011 (Pennsylvania USA water fish mussels riparianfloodplain resources)
Habitat supply models Canter and Atkinson 2011 (USA multiple species) Houle et al 2010 (Quebec CAN gray wolf)Johnson et al 2005 (Northwest Territories CAN caribou gray wolf grizzly bear wolverine) Maceet al 1999 (Montana USA grizzly bear) Roumldder et al 2016 (Cologne Germany Sand Lizard)
Empirical species-stressor models Sawyer et al 2009 (Wyoming USA mule deer) Houle et al 2010 (Quebec CAN gray wolf) Salice et al2011 (South Carolina USA Eastern Narrow-mouthed Toad) Salice 2012 (southeast USA EasternNarrow-mouthed Toad) Wilson et al 2013 (northwest Alaska USA caribou) Goodale andMilman2019 (offshore regions waterfowl waterbirds landbirds) Hodgson et al 2017 (various locationsinsects birds fish ungulates) Jones et al 2017 (Muskoka RiverWatershed CAN benthic invertebrates)Fisher and Burton 2018 (northeast Alberta CAN gray wolf white-tailed deermoose American blackbear coyote Canada lynx fisher red fox snowshoe hare American red squirrel) Claireau et al 2019(western France 10 bat species) Daniel and Koper 2019 (southern Alberta CAN 5 grassland landbirdspecies) Heinrichs et al 2019 (Wyoming USA Greater Sage-Grouse) Mahon et al 2019 (northeastAlberta CAN 27 boreal landbird species) Katzner et al 2020 (California USA roadrunner hawk)Leston et al 2020 (northeast Alberta CAN 20 boreal landbird species)
McNay et al 2006 (north-central British Columbia CAN caribou) Steventon et al 2006 (north coastof British Columbia CAN Marbled Murrelet) Howes et al 2010 (eastern Australia woodlandlandbirds) MacCracken et al 2013 (Bering and Chukchi Seas Pacific walrus) Ban et al 2014 (GreatBarrier Reef Australia reef coral) Fortin et al 2016 (Alaska USA brown bears) Mantyka-Pringleet al 2016 (South East Queensland Australia macroinvertebrates fish) Mantyka-Pringle et al 2017(Slave River Delta Northwest Territories CAN fish waterfowl mammals)
206 Environ Rev Vol 29 2021
Published by NRC Research Press
Env
iron
Rev
Dow
nloa
ded
from
cdn
scie
ncep
ubc
om b
y B
AT
TE
LL
E P
AC
IFIC
NW
NA
TL
LA
B o
n 12
07
21Fo
r pe
rson
al u
se o
nly
Tab
le4
Summaryco
mparison
ofan
alytical
method
sforcu
mulative
effectsassessmen
t(CEA)for
terrestrialspecies
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Cumulative
effectsof
stressors
bas
edon
Exp
ertknow
ledge
literature
review
oflikelyeffects
Estim
ated
chan
gein
amou
ntof
suitab
lehab
itat
forsp
eciesas
aresu
ltof
stressorssu
itab
lehab
itat
isch
arac
terize
dusingex
pert
opinionliterature
and(or)
empirical
data
Directmea
suremen
tsof
speciesrsquo
resp
onse
tograd
ientof
stressors
andco
mbinationsof
stressors
Mod
eled
respon
seof
speciesab
unda
nce
orhab
itat
tocombined
hab
itatstressor
andother
inpu
ts(in
putsmay
bequ
alitativeor
empiricald
ata)
Most
commonly
asso
ciated
with
Project-based
CEA
Project-based
CEAscien
tific
research
Effects-based
CEAscien
tific
research
Effects-based
CEAscien
tificresearch
Key
strength
s
Limited
data
time
andco
strequirem
ents
Welle
stab
lish
edap
proach
Addresses
allo
rmostCEAgo
als
question
susingdirectmea
sures
ofsp
ecies
stressor
relation
ship
Can
incorporatemultipleda
tatype
san
dsources(qua
litativean
dqu
antitative
data)
Can
includeawiderange
ofstressors
Allow
squan
tification
ofeffects
(egloss
ofsu
itab
lehab
itat)w
hen
directsp
eciesndashstressor
resp
onse
orsp
eciesdataarelimited
Other
strengthslimitationsba
sedon
theex
tentto
whichqu
alitativeversus
quan
titative
dataareused
Key
limitations
Fa
ilsto
answ
erCEAquestion
sor
doe
sso
withuncertainreliab
ility
Inco
rporates
effectson
lydueto
hab
itat
loss
Highdata
timean
dco
strequirem
ents
Infreq
uen
tuse
interrestrialsystems
may
limitap
plication
Accuracy
andreliab
ilityhighly
dep
enden
ton
qualityan
dprecision
ofsp
ecies
hab
itat
associations
Restrictedto
speciesthat
can
beread
ilysampled
Fa
ilsto
address
man
yCEA
objectives
(interactionssh
ape
ofsp
eciesndashstressor
resp
onse)
Quan
titative
datarequirem
ents
Spec
ies
Nonemdash
Not
quan
titative
Lo
wmdash
Ifbased
onqualitative
species
hab
itat
associations
Veryhighmdash
Req
uires
species
metrics
across
fullgrad
ientof
stressors(andco
mbinationsif
testingforinteractions)
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareusedin
themod
el
Highmdash
Ifbased
onem
pirical
data
species
hab
itat
associations
(see
text)
Hab
itat
mdashsp
atiald
ata
Nonemdash
Not
quan
titative
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Stressormdash
spatiald
ata
Nonemdash
Not
quan
titative
Highto
very
highmdash
Tobe
effectiverequires
accu
rate
accessible
spatiald
ataon
ldquoindividualrdquostressorsoftenbroad
sector
catego
ries
(mining
oilg
as)
areused
Veryhighmdash
Tobeeffective
requires
accu
ratea
ccessible
spatiald
ataon
ldquoindividualrdquo
stressors
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Mahon and Pelech 207
Published by NRC Research Press
Env
iron
Rev
Dow
nloa
ded
from
cdn
scie
ncep
ubc
om b
y B
AT
TE
LL
E P
AC
IFIC
NW
NA
TL
LA
B o
n 12
07
21Fo
r pe
rson
al u
se o
nly
Tab
le4(continu
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
answ
erkey
CEAquestionsan
dap
plica
tionofCEA
What
isth
erelative
mag
nitudean
ddirection
ofindividual
stressor
effects
Xmdash
Only
based
onqualitativedata
Based
onrelative
contribution
ofstressor
toreducingam
ountof
estimated
suitab
lehab
itat
(req
uires
ldquobackfillingrdquo
individual
stressorsin
GISsee
text)
Pa
rameter
estimates
inmod
elrepresentmag
nitudedirection
and
certainty
ofstressor
effects
Stressor
effectstypically
are
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Relativeco
ntribution
ofstressors
toloss
ofsu
itab
lehab
itat
canbe
analysed
(req
uires
ldquoback-fillingrdquo
individual
stressorsin
GISsee
text)
What
isth
esh
apeofth
esp
eciesndashstressorresp
onse
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edNon
linea
rrelation
ships
(polyn
omial)canbetested
via
mod
elselectionan
dread
ily
grap
hed
Sh
apeof
theresp
onse
canbe
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Dostressors
combin
ead
ditivelyorin
tera
ctively
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edInteractionterm
scanbetested
forinclusion
viamod
elselection
procedures
Interactioneffectscanbeestimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rceof
inputmay
bequalitativeinform
ationor
empirical
mod
els)
Coe
fficien
tsgraphscanbeusedto
catego
rize
synergisticve
rsus
antago
nisticeffects
What
isth
eove
rallim
pac
tofcu
mulative
effectson
populations
Xmdash
Only
based
onqualitative
data
Based
onloss
ofsu
itab
lehab
itat
Sp
eciesndashhab
itatndashstressor
mod
els
canbeusedto
predictan
dco
mpare
speciesab
undan
cemea
sures
amon
gldquon
o-disturban
cereferen
cerdquo
curren
tan
dfuture
landscap
eco
nditionsbased
onGISla
ndscap
esimulation
Po
pulation
resp
onseoutputcanbe
compared
amon
gldquon
o-disturban
ce
referencerdquocu
rren
tan
dfuture
scen
arioswithmod
elinput(hab
itat
stressor
amou
nts)b
ased
onGIS
landscap
esimulation
Sp
eciesndashhab
itat
mod
elprediction
sforhab
itat
amou
nts
orsp
ecies
abunda
nce
canbe
compa
redam
ong
ldquono-disturban
cereferen
cerdquocu
rren
tan
dfuture
landscapecondition
sba
sedon
GISla
ndscapesimulation
What
isth
eove
rallim
pac
tonth
eco
mmunity
Xmdash
Only
based
onqualitative
data
Req
uires
calculatingspeciesndashhab
itat
mod
elsforallspeciescalculating
predicted
abunda
ncesin
reference
curren
tor
future
landscapesand
comparingcommunitymetrics
amon
gthesepredictedcommun
ities
Withsu
fficien
tdata
community
metrics
canbeco
mpared
amon
glowh
ighstressor
catego
ries
Req
uires
calculatingpop
ulation
resp
onsesforsp
eciesin
reference
curren
tor
future
landscap
esa
nd
comparingco
mmunitymetrics
amon
gthesepredictedco
mmunities
Com
munitymetrics
cansimilarly
becompa
redusingspeciesabun
dance
inreference
curren
tor
future
landscap
esa
spredictedfrom
empiricalspeciesndashhab
itatndashstressor
mod
els
How
certainar
eresu
lts
How
cance
rtainty
be
assessed
Verylow
mdashSu
bjectivenature
mak
esitdifficu
ltto
quan
tify
certainty
Verylowmdash
Ifspeciesndashhab
itat
basedon
qualitativeinform
ation(subjective
nature
mak
esitdifficu
ltto
quan
tify
certainty)
Highmdash
Statisticalsignificance
ofstressor
effectscanbeestimated
viaco
nfiden
ceintervalsof
param
eter
estimates
Moderateto
highmdash
Dep
endson
extent
towhichmod
elsareba
sedon
qualitativeversusem
piricalda
ta
Mod
eratemdash
Ifspecies
habitat
relation
shipsbasedon
empiricald
ata
Approachallowsuncertainty
ofrelation
shipsto
bequan
tified
and
inputinto
themod
els
Ran
geofstressors
evaluated
Highmdash
Not
limited
bysp
atial
dataon
stressors
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
andaccessible
spatiald
ata
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
and
accessible
spatiald
ata
Highmdash
Not
necessarily
limited
by
spatiald
ataon
stressorsifmod
els
based
onqualitativerelation
ships
208 Environ Rev Vol 29 2021
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In more recent and complex approaches empirical speciesndashhabitat models are developed by sampling terrestrial speciesacross habitat types in a study area and using advanced statisticalanalyses to quantify species associations with different habitatsModels are termed resource selection functions (RSF) when spe-cies data (presenceabsence usedunused) are analyzed at smallwithin-population scales and species distribution models (SDM)or species abundance models (SAM) when data (occurrence abun-dance respectively) are analyzed at large range-level scales (Guisanand Zimmermann 2000 Guisan and Thuiller 2005 Elith andLeathwick 2009 Hegel et al 2010) For terrestrial birds current andemerging methods can be followed to convert species counts at asample station (the most common sample metric for terrestrialbirds) to a density estimate (Soacutelymos et al 2013 Matsuoka et al2014) Reporting species responses as densities (versus relativeabundance) provides (i) a relevant and readily communicatedmeasure of species response (ii) a standardized measure acrossregions and studies and (iii) an evaluation against conservationor management targets or population viability estimatesA range of statistical modeling frameworks are available to analyse
speciesndashhabitat relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) Typically habitat coefficients in these modelsare used to assign habitat rankings for species Both expert andempirically based habitat rankings require validation againstexternal data (or internal validations in the case of empiricalmodels) to ensure their reliability for an area (Pearce and Ferrier2000 Boyce et al 2002 Muir et al 2011) Once validated modelresults are used to assess the amount and distribution of suitablehabitat for species within a GIS (Millspaugh and Thompson 2009)Additionally when species responses have been converted to den-sity estimates coefficients can be coupled with GIS maps to esti-mate species densities or population sizes Within the context ofCEA the footprint or area of proposed projects existing projectsand disturbances and future land use scenarios can be overlaidon these maps to estimate the change in suitable habitat andassess effects on terrestrial species based on these habitat changeswithin the study area
322 Examples of applicationsWe highlight both project-based CEA and effects-based CEA
examples to demonstrate the flexibility of the habitat supplymethod Project-based CEAs typically use literature-based or hab-itat suitability models to identify map and quantify the area ofsuitable habitat for a given species within the study area We pro-vide a hypothetical example to explain how the habitat supplymethod is typically used to assess project-specific effects (Table 6)Changes in the amount of suitable habitat during the project(application phase eg from 10 to 6 of study area in Table 6)and after the project (closureremediation phase eg 0 changein Table 6) are estimated relative to current or baseline The hab-itat supply method is not typically used to assess CE because theextent to which existing or past stressors have affected pre-existinghabitat types is often not known Instead CE requirements areoften met by comparing the total area of the proposed project (eg5 of study area in Table 6) to the total area of other stressors regard-less of habitat affected (eg 35 Table 6) Thus in high or moder-ately disturbed areas these comparisons may suggest that a projectwould have minimal added effects relative to existing stressorsWhere data and resources allow some project-based CEAs mayuse historic maps or create hypothetical no-disturbance land-scapes within a GIS (eg see methods in ABMI 2017 Mahon et al2019) to assess how other stressors may have affected specific habi-tats However resource requirements (time data technical exper-tise) place this type of assessment beyond themandate or capacity ofmost project-based CEAs We include an example of the habitat sup-ply method from a recent project-based CEA from the IAAC RegistryT
able
4(conclud
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
inco
rpora
tetrad
itional
knowledge
Yes
Notusu
ally
Notusu
ally
Yes
Statistica
ltech
nical
expertise
required
Low
tomoderatemdash
Mod
eratewith
form
almod
eling
Moderate
High
Highto
very
high(specialized)
Tim
efra
me
Month
s
Month
sto
ltyea
rmdash
For
speciesndashhab
itat
mod
elsbased
onqualitativedata(dep
enden
ton
statusof
spatiald
ata
detailo
fthe
mod
ela
vailab
ilityof
species
inform
ation)
gtYea
rto
multi-ye
ar
Speciesdataco
llection
may
nee
dto
occu
rov
ergt1ye
arto
provide
sufficien
tsamples
Month
sto
gtmulti-ye
armdash
Highly
dep
enden
ton
sourceof
datainput
(existingdata
new
empirical
data
collection
)
Statisticalm
odel
dev
elop
men
tan
drefinem
entcanbetime
consu
ming
Modelingap
proachmay
require
additional
wee
ksor
month
s
gtYea
rmdash
Forem
piricalspe
ciesndashh
abitat
mod
elsthat
requ
irenew
data
collection
Dep
enden
ton
timerequired
toco
mpile
evaluatean
dcatego
rize
hab
itat
andstressor
data
Mahon and Pelech 209
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se o
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Tab
le5
Exa
mple
ofaqualitativereview
(matrixtable)for
aproject-based
CEA
Valued
compon
ent
Potential
effects
Mitigation
compen
sation
Residual
effect
Sign
ifican
ceof
residual
effects
Eco
logical
societal
value
Ove
rallsign
ifican
ceMag
nitude
Spatiale
xten
t
Freq
uen
cy
probab
ility
Duration
Rev
ersibility
Graywolf
Assoc
iatedwith
major
preysp
ecies
(moo
sed
eer)A
t
closure
increa
sed
area
sof
grasslan
d
willc
reatewhite-
tailed
dee
rhab
itat
andpoten
tially
higher
dee
ran
d
wolfnumbers
Limitfootprintof
disturbed
area
s
Increase
indeeran
d
wolfp
opulation
s
over
timeResidual
adverseeffect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
highqualitymoo
se
hab
itattherefore
limited
wolf
pop
ulation
MIN
IMAL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
is
long-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
is
reversibledu
ring
site
reclam
ation
Moo
sefollow
edby
wolvessh
ould
return
tosite
at
curren
tlevels
LOW
Wolvesarecommon
inthelocaland
region
alstudy
areasLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
wolve
s
aremob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
American
black
bea
r
Initialm
igration
duringsite
preparationan
d
constru
ction
return
tosite
area
afterperiodof
human
noise
hab
ituation
Limitfootprintof
disturbed
areas
Establishwildlife
policyto
minim
ize
human
interaction
withwildlifean
d
decrease
poten
tial
forhab
ituation
Increa
sedseason
al
use
ofthesite
over
timedep
endson
succession
of
reclaimed
area
s
Residual
adve
rse
effect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
abov
eav
erag
ebea
r
hab
itatM
INIM
AL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
islong-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
reversibledu
ring
site
reclam
ation
New
grasslan
d
hab
itat
may
increase
the
season
alhab
itat
use
bybe
ars
LOW
Bearde
nsity
inthe
study
site
islikely
comparab
leto
that
inthesurrou
nding
landscapeLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
bea
rsare
mob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
NoteT
hetable
summarizes
theproposed
project
effectsmitigationan
dco
mpen
sation
mea
surespoten
tial
residual
effects(theen
vironmen
tale
ffectof
aproject
that
remainsaftermitigationmea
sureshav
ebee
nim
plemen
ted)sign
ificance
ofresidual
effectsin
considerationof
ava
rietyof
assessmen
tfactors(ratings
inboldface
type)a
ndwhether
asign
ificantad
verseen
vironmen
tale
ffecton
ava
lued
compon
entisex
pected
asaresu
ltof
project
implemen
tation
Sou
rceEnvironmen
talImpactStatem
entfortheMarathon
PGM-CuProject20
12Thetable
represents
description
san
dratings
summarizingtheen
vironmen
tale
ffects
ofametal
minedev
elop
men
ton
twova
lued
compon
ents
(graywolfan
dAmerican
black
bea
r)
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(Environmental Impact Statement for the Springbank Off-streamReservoir Project 2018) to estimate area changes in the amountof suitable habitat (based on habitat suitability models) for selectVCs Spraguersquos Pipit (Anthus spragueii) and Northern Leopard Frog(Lithobates pipiens) during different flood scenarios created by waterdiversion to a channel and reservoir within the proposed projectarea (Table 7)Effects-based CEAs typically involve the creation and application
of empirical speciesndashhabitat models within the study area Maceet al (1999) evaluated the CE of human and resource activity ongrizzly bear (Ursus arctos) habitat inwesternMontana during springsummer and fall seasons The authors modelled bear resourceselection from telemetry data satellite imagery elevation humanactivity points roads and trails and extrapolatedmodels to a largerregion with similar climate and vegetation Quantifying and map-ping areas with a high probability of use by bears in each seasonwas used to inform conservation and management actions (egroad or trail closures within important seasonal habitats habitatrestoration future road creation) Roumldder et al (2016) outline aninnovative approach to integrate SDM and potential connectivitymodels to identify connected and isolated populations and habitatcorridors for a species of conservation concern the Sand Lizard(Lacerta agilis) in western Germany (Fig 2) Quantifying potentialconnectivity areas (for movement like dispersal or migration) wasused to identify areas of strong connectivity persistent isolationand stable connective networks needed to connect populationsThis proposed framework may be particularly useful for proposeddevelopments that affect (i) portions of a previously larger inter-connected population or (ii) connective features between perma-nently colonized habitat patches Additional examples from thepublished literature are listed in Table 3
323 Advantages and limitations CEA questions and whenwhyto use3231 AdvantagesAdvantages of the habitat supply method include its ability to
quantify effects using loss of suitable habitat This approach
which has wide-spread application in wildlife research may besuitable when (i) data to map and measure habitat change areavailable but empirical data on species response to specific stres-sors are limited (ii) collaborative approaches focused on localissues and values are important (eg speciesndashhabitat associa-tions may be based on both qualitative (TK local and expertknowledge) and quantitative (speciesndashhabitat models) data) and(iii) data to develop empirical speciesndashhabitat models and adjusteddensity estimates are available so that total impacts can be com-pared to current and future population targets (eg Mahon et al2014)
3232 LimitationsLimitations to the habitat supply modelling approach include
the availability of accurate publicly available spatial data forhabitats and stressors and reliable specieshabitat associationsfrom the region of interest If additional data are required torefine existing habitat suitability models or create new empiricalspeciesndashhabitat models within a study area stakeholders or part-ners should a priori develop rigorous sampling designs and sur-vey protocols to meet project objectives We note two additionalcautions when using this approach Specieshabitat associationsbased on limited empirical data from within the region or datafrom outside the region require model validation and testing(Muir et al 2011) Finally stressor effects are limited to how stres-sors contribute to the loss of suitable habitat (habitat changesare used to infer population consequences) Habitat-based analy-sis alone may not be useful because (i) habitat is assumed to bea useful measure of population status (ii) habitat needs of the tar-get species or VC are assumed to be known (Schultz 2010) and(iii) specific stressor effects on the target species or VC are notquantified (eg the shape magnitude and direction of speciesrsquoresponse to stressors indirect or novel effects of stressors on speciesor interactions among stressors) Failure to identify which stressorsare influencing target species or VCsmay limit future research intobiological mechanisms (eg habitat selection movement disper-sal reproduction survival predatorndashprey dynamics) needed to
Fig 1 Example of a qualitative review (conceptual modeldiagram) for an effects-based CEA Figure represents an annual life cycle ofevents of Greater Sage-Grouse responding to oil and gas development in southwest Wyoming USA (from Heinrichs et al 2019 reproducedwith permission of Ecol Appl Vol 29 copy2019 Ecological Society of America)
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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
212 Environ Rev Vol 29 2021
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
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Mahon and Pelech 221
Published by NRC Research Press
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iron
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n 12
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Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
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Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
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222 Environ Rev Vol 29 2021
Published by NRC Research Press
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iron
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n 12
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Mahon and Pelech 223
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r pe
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Wintle BA Bekessy SA Venier LA Pearce JL and Chisholm RA 2005Utility of dynamic-landscape metapopulation models for sustainable forestmanagement Conserv Biol 19 1930ndash1943 doi101111j1523-1739200500276x
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Yukon Environmental and Socio-economic Assessment Board (YESAB) 2019Consideration of Cumulative Effects in YESAB Assessments July 2019 ReportAvailable from httpsyesabca [last accessed 20 August 2020]
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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[00000000]AutoFilterGrayImages trueEncodeColorImages trueAlwaysEmbed []EndPage -1DownsampleColorImages trueASCII85EncodePages falsePreserveEPSInfo falsePDFXTrimBoxToMediaBoxOffset [00000000]CompatibilityLevel 13MonoImageResolution 600NeverEmbed [Arial-BlackArial-BlackItalicArial-BoldItalicMTArial-BoldMTArial-ItalicMTArialMTArialNarrowArialNarrow-BoldArialNarrow-BoldItalicArialNarrow-ItalicArialUnicodeMSCenturyGothicCenturyGothic-BoldCenturyGothic-BoldItalicCenturyGothic-ItalicCourierNewPS-BoldItalicMTCourierNewPS-BoldMTCourierNewPS-ItalicMTCourierNewPSMTGeorgiaGeorgia-BoldGeorgia-BoldItalicGeorgia-ItalicImpactLucidaConsoleTahomaTahoma-BoldTimesNewRomanMT-ExtraBoldTimesNewRomanPS-BoldItalicMTTimesNewRomanPS-BoldMTTimesNewRomanPS-ItalicMTTimesNewRomanPSMTTrebuchet-BoldItalicTrebuchetMSTrebuchetMS-BoldTrebuchetMS-ItalicVerdanaVerdana-BoldVerdana-BoldItalicVerdana-Italic]CannotEmbedFontPolicy WarningAutoPositionEPSFiles truePreserveOPIComments falseJPEG2000GrayACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtDSCReportingLevel 0PDFXOutputConditionIdentifier ()PDFXCompliantPDFOnly falseAllowTransparency falsePreserveCopyPage trueUsePrologue falseStartPage 1MonoImageDownsampleThreshold 10GrayImageDownsampleThreshold 10CheckCompliance [None]CreateJDFFile falsePDFXSetBleedBoxToMediaBox trueEmbedOpenType falseOPM 0PreserveOverprintSettings falseUCRandBGInfo RemoveColorImageDownsampleThreshold 10MonoImageDict ltltK -1gtgtGrayImageDownsampleType AverageDescription ltltENU (Use these settings to create Adobe PDF documents suitable for reliable viewing and printing of business documents Created PDF documents can be opened with Acrobat and Adobe Reader 50 and later)PTB 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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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ltFEFF4f7f752890194e9b8a2d7f6e5efa7acb7684002000410064006f006200650020005000440046002065874ef69069752865bc666e901a554652d965874ef6768467e5770b548c52175370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c4f86958b555f5df25efa7acb76840020005000440046002065874ef63002gtgtgtCropMonoImages trueDefaultRenderingIntent RelativeColorimetericPreserveHalftoneInfo falseColorImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtCropGrayImages truePDFXOutputCondition ()SubsetFonts trueEncodeMonoImages trueCropColorImages truePDFXNoTrimBoxError truegtgtsetdistillerparamsltltPageSize [61207920]HWResolution [600600]gtgtsetpagedevice
Tab
le4
Summaryco
mparison
ofan
alytical
method
sforcu
mulative
effectsassessmen
t(CEA)for
terrestrialspecies
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Cumulative
effectsof
stressors
bas
edon
Exp
ertknow
ledge
literature
review
oflikelyeffects
Estim
ated
chan
gein
amou
ntof
suitab
lehab
itat
forsp
eciesas
aresu
ltof
stressorssu
itab
lehab
itat
isch
arac
terize
dusingex
pert
opinionliterature
and(or)
empirical
data
Directmea
suremen
tsof
speciesrsquo
resp
onse
tograd
ientof
stressors
andco
mbinationsof
stressors
Mod
eled
respon
seof
speciesab
unda
nce
orhab
itat
tocombined
hab
itatstressor
andother
inpu
ts(in
putsmay
bequ
alitativeor
empiricald
ata)
Most
commonly
asso
ciated
with
Project-based
CEA
Project-based
CEAscien
tific
research
Effects-based
CEAscien
tific
research
Effects-based
CEAscien
tificresearch
Key
strength
s
Limited
data
time
andco
strequirem
ents
Welle
stab
lish
edap
proach
Addresses
allo
rmostCEAgo
als
question
susingdirectmea
sures
ofsp
ecies
stressor
relation
ship
Can
incorporatemultipleda
tatype
san
dsources(qua
litativean
dqu
antitative
data)
Can
includeawiderange
ofstressors
Allow
squan
tification
ofeffects
(egloss
ofsu
itab
lehab
itat)w
hen
directsp
eciesndashstressor
resp
onse
orsp
eciesdataarelimited
Other
strengthslimitationsba
sedon
theex
tentto
whichqu
alitativeversus
quan
titative
dataareused
Key
limitations
Fa
ilsto
answ
erCEAquestion
sor
doe
sso
withuncertainreliab
ility
Inco
rporates
effectson
lydueto
hab
itat
loss
Highdata
timean
dco
strequirem
ents
Infreq
uen
tuse
interrestrialsystems
may
limitap
plication
Accuracy
andreliab
ilityhighly
dep
enden
ton
qualityan
dprecision
ofsp
ecies
hab
itat
associations
Restrictedto
speciesthat
can
beread
ilysampled
Fa
ilsto
address
man
yCEA
objectives
(interactionssh
ape
ofsp
eciesndashstressor
resp
onse)
Quan
titative
datarequirem
ents
Spec
ies
Nonemdash
Not
quan
titative
Lo
wmdash
Ifbased
onqualitative
species
hab
itat
associations
Veryhighmdash
Req
uires
species
metrics
across
fullgrad
ientof
stressors(andco
mbinationsif
testingforinteractions)
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareusedin
themod
el
Highmdash
Ifbased
onem
pirical
data
species
hab
itat
associations
(see
text)
Hab
itat
mdashsp
atiald
ata
Nonemdash
Not
quan
titative
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Highmdash
Usu
ally
detailed
vege
tation
inve
ntory
laye
rs
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Stressormdash
spatiald
ata
Nonemdash
Not
quan
titative
Highto
very
highmdash
Tobe
effectiverequires
accu
rate
accessible
spatiald
ataon
ldquoindividualrdquostressorsoftenbroad
sector
catego
ries
(mining
oilg
as)
areused
Veryhighmdash
Tobeeffective
requires
accu
ratea
ccessible
spatiald
ataon
ldquoindividualrdquo
stressors
Req
uirem
ents
based
ontheex
tentto
whichqualitativeve
rsusquan
titative
dataareused
Mahon and Pelech 207
Published by NRC Research Press
Env
iron
Rev
Dow
nloa
ded
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r pe
rson
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nly
Tab
le4(continu
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
answ
erkey
CEAquestionsan
dap
plica
tionofCEA
What
isth
erelative
mag
nitudean
ddirection
ofindividual
stressor
effects
Xmdash
Only
based
onqualitativedata
Based
onrelative
contribution
ofstressor
toreducingam
ountof
estimated
suitab
lehab
itat
(req
uires
ldquobackfillingrdquo
individual
stressorsin
GISsee
text)
Pa
rameter
estimates
inmod
elrepresentmag
nitudedirection
and
certainty
ofstressor
effects
Stressor
effectstypically
are
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Relativeco
ntribution
ofstressors
toloss
ofsu
itab
lehab
itat
canbe
analysed
(req
uires
ldquoback-fillingrdquo
individual
stressorsin
GISsee
text)
What
isth
esh
apeofth
esp
eciesndashstressorresp
onse
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edNon
linea
rrelation
ships
(polyn
omial)canbetested
via
mod
elselectionan
dread
ily
grap
hed
Sh
apeof
theresp
onse
canbe
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Dostressors
combin
ead
ditivelyorin
tera
ctively
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edInteractionterm
scanbetested
forinclusion
viamod
elselection
procedures
Interactioneffectscanbeestimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rceof
inputmay
bequalitativeinform
ationor
empirical
mod
els)
Coe
fficien
tsgraphscanbeusedto
catego
rize
synergisticve
rsus
antago
nisticeffects
What
isth
eove
rallim
pac
tofcu
mulative
effectson
populations
Xmdash
Only
based
onqualitative
data
Based
onloss
ofsu
itab
lehab
itat
Sp
eciesndashhab
itatndashstressor
mod
els
canbeusedto
predictan
dco
mpare
speciesab
undan
cemea
sures
amon
gldquon
o-disturban
cereferen
cerdquo
curren
tan
dfuture
landscap
eco
nditionsbased
onGISla
ndscap
esimulation
Po
pulation
resp
onseoutputcanbe
compared
amon
gldquon
o-disturban
ce
referencerdquocu
rren
tan
dfuture
scen
arioswithmod
elinput(hab
itat
stressor
amou
nts)b
ased
onGIS
landscap
esimulation
Sp
eciesndashhab
itat
mod
elprediction
sforhab
itat
amou
nts
orsp
ecies
abunda
nce
canbe
compa
redam
ong
ldquono-disturban
cereferen
cerdquocu
rren
tan
dfuture
landscapecondition
sba
sedon
GISla
ndscapesimulation
What
isth
eove
rallim
pac
tonth
eco
mmunity
Xmdash
Only
based
onqualitative
data
Req
uires
calculatingspeciesndashhab
itat
mod
elsforallspeciescalculating
predicted
abunda
ncesin
reference
curren
tor
future
landscapesand
comparingcommunitymetrics
amon
gthesepredictedcommun
ities
Withsu
fficien
tdata
community
metrics
canbeco
mpared
amon
glowh
ighstressor
catego
ries
Req
uires
calculatingpop
ulation
resp
onsesforsp
eciesin
reference
curren
tor
future
landscap
esa
nd
comparingco
mmunitymetrics
amon
gthesepredictedco
mmunities
Com
munitymetrics
cansimilarly
becompa
redusingspeciesabun
dance
inreference
curren
tor
future
landscap
esa
spredictedfrom
empiricalspeciesndashhab
itatndashstressor
mod
els
How
certainar
eresu
lts
How
cance
rtainty
be
assessed
Verylow
mdashSu
bjectivenature
mak
esitdifficu
ltto
quan
tify
certainty
Verylowmdash
Ifspeciesndashhab
itat
basedon
qualitativeinform
ation(subjective
nature
mak
esitdifficu
ltto
quan
tify
certainty)
Highmdash
Statisticalsignificance
ofstressor
effectscanbeestimated
viaco
nfiden
ceintervalsof
param
eter
estimates
Moderateto
highmdash
Dep
endson
extent
towhichmod
elsareba
sedon
qualitativeversusem
piricalda
ta
Mod
eratemdash
Ifspecies
habitat
relation
shipsbasedon
empiricald
ata
Approachallowsuncertainty
ofrelation
shipsto
bequan
tified
and
inputinto
themod
els
Ran
geofstressors
evaluated
Highmdash
Not
limited
bysp
atial
dataon
stressors
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
andaccessible
spatiald
ata
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
and
accessible
spatiald
ata
Highmdash
Not
necessarily
limited
by
spatiald
ataon
stressorsifmod
els
based
onqualitativerelation
ships
208 Environ Rev Vol 29 2021
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In more recent and complex approaches empirical speciesndashhabitat models are developed by sampling terrestrial speciesacross habitat types in a study area and using advanced statisticalanalyses to quantify species associations with different habitatsModels are termed resource selection functions (RSF) when spe-cies data (presenceabsence usedunused) are analyzed at smallwithin-population scales and species distribution models (SDM)or species abundance models (SAM) when data (occurrence abun-dance respectively) are analyzed at large range-level scales (Guisanand Zimmermann 2000 Guisan and Thuiller 2005 Elith andLeathwick 2009 Hegel et al 2010) For terrestrial birds current andemerging methods can be followed to convert species counts at asample station (the most common sample metric for terrestrialbirds) to a density estimate (Soacutelymos et al 2013 Matsuoka et al2014) Reporting species responses as densities (versus relativeabundance) provides (i) a relevant and readily communicatedmeasure of species response (ii) a standardized measure acrossregions and studies and (iii) an evaluation against conservationor management targets or population viability estimatesA range of statistical modeling frameworks are available to analyse
speciesndashhabitat relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) Typically habitat coefficients in these modelsare used to assign habitat rankings for species Both expert andempirically based habitat rankings require validation againstexternal data (or internal validations in the case of empiricalmodels) to ensure their reliability for an area (Pearce and Ferrier2000 Boyce et al 2002 Muir et al 2011) Once validated modelresults are used to assess the amount and distribution of suitablehabitat for species within a GIS (Millspaugh and Thompson 2009)Additionally when species responses have been converted to den-sity estimates coefficients can be coupled with GIS maps to esti-mate species densities or population sizes Within the context ofCEA the footprint or area of proposed projects existing projectsand disturbances and future land use scenarios can be overlaidon these maps to estimate the change in suitable habitat andassess effects on terrestrial species based on these habitat changeswithin the study area
322 Examples of applicationsWe highlight both project-based CEA and effects-based CEA
examples to demonstrate the flexibility of the habitat supplymethod Project-based CEAs typically use literature-based or hab-itat suitability models to identify map and quantify the area ofsuitable habitat for a given species within the study area We pro-vide a hypothetical example to explain how the habitat supplymethod is typically used to assess project-specific effects (Table 6)Changes in the amount of suitable habitat during the project(application phase eg from 10 to 6 of study area in Table 6)and after the project (closureremediation phase eg 0 changein Table 6) are estimated relative to current or baseline The hab-itat supply method is not typically used to assess CE because theextent to which existing or past stressors have affected pre-existinghabitat types is often not known Instead CE requirements areoften met by comparing the total area of the proposed project (eg5 of study area in Table 6) to the total area of other stressors regard-less of habitat affected (eg 35 Table 6) Thus in high or moder-ately disturbed areas these comparisons may suggest that a projectwould have minimal added effects relative to existing stressorsWhere data and resources allow some project-based CEAs mayuse historic maps or create hypothetical no-disturbance land-scapes within a GIS (eg see methods in ABMI 2017 Mahon et al2019) to assess how other stressors may have affected specific habi-tats However resource requirements (time data technical exper-tise) place this type of assessment beyond themandate or capacity ofmost project-based CEAs We include an example of the habitat sup-ply method from a recent project-based CEA from the IAAC RegistryT
able
4(conclud
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
inco
rpora
tetrad
itional
knowledge
Yes
Notusu
ally
Notusu
ally
Yes
Statistica
ltech
nical
expertise
required
Low
tomoderatemdash
Mod
eratewith
form
almod
eling
Moderate
High
Highto
very
high(specialized)
Tim
efra
me
Month
s
Month
sto
ltyea
rmdash
For
speciesndashhab
itat
mod
elsbased
onqualitativedata(dep
enden
ton
statusof
spatiald
ata
detailo
fthe
mod
ela
vailab
ilityof
species
inform
ation)
gtYea
rto
multi-ye
ar
Speciesdataco
llection
may
nee
dto
occu
rov
ergt1ye
arto
provide
sufficien
tsamples
Month
sto
gtmulti-ye
armdash
Highly
dep
enden
ton
sourceof
datainput
(existingdata
new
empirical
data
collection
)
Statisticalm
odel
dev
elop
men
tan
drefinem
entcanbetime
consu
ming
Modelingap
proachmay
require
additional
wee
ksor
month
s
gtYea
rmdash
Forem
piricalspe
ciesndashh
abitat
mod
elsthat
requ
irenew
data
collection
Dep
enden
ton
timerequired
toco
mpile
evaluatean
dcatego
rize
hab
itat
andstressor
data
Mahon and Pelech 209
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r pe
rson
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se o
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Tab
le5
Exa
mple
ofaqualitativereview
(matrixtable)for
aproject-based
CEA
Valued
compon
ent
Potential
effects
Mitigation
compen
sation
Residual
effect
Sign
ifican
ceof
residual
effects
Eco
logical
societal
value
Ove
rallsign
ifican
ceMag
nitude
Spatiale
xten
t
Freq
uen
cy
probab
ility
Duration
Rev
ersibility
Graywolf
Assoc
iatedwith
major
preysp
ecies
(moo
sed
eer)A
t
closure
increa
sed
area
sof
grasslan
d
willc
reatewhite-
tailed
dee
rhab
itat
andpoten
tially
higher
dee
ran
d
wolfnumbers
Limitfootprintof
disturbed
area
s
Increase
indeeran
d
wolfp
opulation
s
over
timeResidual
adverseeffect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
highqualitymoo
se
hab
itattherefore
limited
wolf
pop
ulation
MIN
IMAL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
is
long-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
is
reversibledu
ring
site
reclam
ation
Moo
sefollow
edby
wolvessh
ould
return
tosite
at
curren
tlevels
LOW
Wolvesarecommon
inthelocaland
region
alstudy
areasLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
wolve
s
aremob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
American
black
bea
r
Initialm
igration
duringsite
preparationan
d
constru
ction
return
tosite
area
afterperiodof
human
noise
hab
ituation
Limitfootprintof
disturbed
areas
Establishwildlife
policyto
minim
ize
human
interaction
withwildlifean
d
decrease
poten
tial
forhab
ituation
Increa
sedseason
al
use
ofthesite
over
timedep
endson
succession
of
reclaimed
area
s
Residual
adve
rse
effect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
abov
eav
erag
ebea
r
hab
itatM
INIM
AL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
islong-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
reversibledu
ring
site
reclam
ation
New
grasslan
d
hab
itat
may
increase
the
season
alhab
itat
use
bybe
ars
LOW
Bearde
nsity
inthe
study
site
islikely
comparab
leto
that
inthesurrou
nding
landscapeLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
bea
rsare
mob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
NoteT
hetable
summarizes
theproposed
project
effectsmitigationan
dco
mpen
sation
mea
surespoten
tial
residual
effects(theen
vironmen
tale
ffectof
aproject
that
remainsaftermitigationmea
sureshav
ebee
nim
plemen
ted)sign
ificance
ofresidual
effectsin
considerationof
ava
rietyof
assessmen
tfactors(ratings
inboldface
type)a
ndwhether
asign
ificantad
verseen
vironmen
tale
ffecton
ava
lued
compon
entisex
pected
asaresu
ltof
project
implemen
tation
Sou
rceEnvironmen
talImpactStatem
entfortheMarathon
PGM-CuProject20
12Thetable
represents
description
san
dratings
summarizingtheen
vironmen
tale
ffects
ofametal
minedev
elop
men
ton
twova
lued
compon
ents
(graywolfan
dAmerican
black
bea
r)
210 Environ Rev Vol 29 2021
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(Environmental Impact Statement for the Springbank Off-streamReservoir Project 2018) to estimate area changes in the amountof suitable habitat (based on habitat suitability models) for selectVCs Spraguersquos Pipit (Anthus spragueii) and Northern Leopard Frog(Lithobates pipiens) during different flood scenarios created by waterdiversion to a channel and reservoir within the proposed projectarea (Table 7)Effects-based CEAs typically involve the creation and application
of empirical speciesndashhabitat models within the study area Maceet al (1999) evaluated the CE of human and resource activity ongrizzly bear (Ursus arctos) habitat inwesternMontana during springsummer and fall seasons The authors modelled bear resourceselection from telemetry data satellite imagery elevation humanactivity points roads and trails and extrapolatedmodels to a largerregion with similar climate and vegetation Quantifying and map-ping areas with a high probability of use by bears in each seasonwas used to inform conservation and management actions (egroad or trail closures within important seasonal habitats habitatrestoration future road creation) Roumldder et al (2016) outline aninnovative approach to integrate SDM and potential connectivitymodels to identify connected and isolated populations and habitatcorridors for a species of conservation concern the Sand Lizard(Lacerta agilis) in western Germany (Fig 2) Quantifying potentialconnectivity areas (for movement like dispersal or migration) wasused to identify areas of strong connectivity persistent isolationand stable connective networks needed to connect populationsThis proposed framework may be particularly useful for proposeddevelopments that affect (i) portions of a previously larger inter-connected population or (ii) connective features between perma-nently colonized habitat patches Additional examples from thepublished literature are listed in Table 3
323 Advantages and limitations CEA questions and whenwhyto use3231 AdvantagesAdvantages of the habitat supply method include its ability to
quantify effects using loss of suitable habitat This approach
which has wide-spread application in wildlife research may besuitable when (i) data to map and measure habitat change areavailable but empirical data on species response to specific stres-sors are limited (ii) collaborative approaches focused on localissues and values are important (eg speciesndashhabitat associa-tions may be based on both qualitative (TK local and expertknowledge) and quantitative (speciesndashhabitat models) data) and(iii) data to develop empirical speciesndashhabitat models and adjusteddensity estimates are available so that total impacts can be com-pared to current and future population targets (eg Mahon et al2014)
3232 LimitationsLimitations to the habitat supply modelling approach include
the availability of accurate publicly available spatial data forhabitats and stressors and reliable specieshabitat associationsfrom the region of interest If additional data are required torefine existing habitat suitability models or create new empiricalspeciesndashhabitat models within a study area stakeholders or part-ners should a priori develop rigorous sampling designs and sur-vey protocols to meet project objectives We note two additionalcautions when using this approach Specieshabitat associationsbased on limited empirical data from within the region or datafrom outside the region require model validation and testing(Muir et al 2011) Finally stressor effects are limited to how stres-sors contribute to the loss of suitable habitat (habitat changesare used to infer population consequences) Habitat-based analy-sis alone may not be useful because (i) habitat is assumed to bea useful measure of population status (ii) habitat needs of the tar-get species or VC are assumed to be known (Schultz 2010) and(iii) specific stressor effects on the target species or VC are notquantified (eg the shape magnitude and direction of speciesrsquoresponse to stressors indirect or novel effects of stressors on speciesor interactions among stressors) Failure to identify which stressorsare influencing target species or VCsmay limit future research intobiological mechanisms (eg habitat selection movement disper-sal reproduction survival predatorndashprey dynamics) needed to
Fig 1 Example of a qualitative review (conceptual modeldiagram) for an effects-based CEA Figure represents an annual life cycle ofevents of Greater Sage-Grouse responding to oil and gas development in southwest Wyoming USA (from Heinrichs et al 2019 reproducedwith permission of Ecol Appl Vol 29 copy2019 Ecological Society of America)
Mahon and Pelech 211
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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
212 Environ Rev Vol 29 2021
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
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Mahon and Pelech 221
Published by NRC Research Press
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iron
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n 12
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Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
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Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
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222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Mahon and Pelech 223
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r pe
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Wilson RR Liebezeit JR and Loya WM 2013 Accounting for uncer-tainty in oil and gas development impacts to wildlife in Alaska ConservLett 6 350ndash358 doi101111conl12016
Wintle BA Bekessy SA Venier LA Pearce JL and Chisholm RA 2005Utility of dynamic-landscape metapopulation models for sustainable forestmanagement Conserv Biol 19 1930ndash1943 doi101111j1523-1739200500276x
Yukon Environmental and Socio-economic Assessment Board (YESAB) 2005Proponentrsquos guide to information requirements for executive committeeproject proposal submissions Available from httpsyesabcawpwp-contentuploads201304Proponents-Guide-to-Info-Requirements-for-EC-Project-Submissionpdf [last accessed 13 November 2018]
Yukon Environmental and Socio-economic Assessment Board (YESAB) 2019Consideration of Cumulative Effects in YESAB Assessments July 2019 ReportAvailable from httpsyesabca [last accessed 20 August 2020]
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset [00000000]AutoFilterGrayImages trueEncodeColorImages trueAlwaysEmbed []EndPage -1DownsampleColorImages trueASCII85EncodePages falsePreserveEPSInfo falsePDFXTrimBoxToMediaBoxOffset [00000000]CompatibilityLevel 13MonoImageResolution 600NeverEmbed [Arial-BlackArial-BlackItalicArial-BoldItalicMTArial-BoldMTArial-ItalicMTArialMTArialNarrowArialNarrow-BoldArialNarrow-BoldItalicArialNarrow-ItalicArialUnicodeMSCenturyGothicCenturyGothic-BoldCenturyGothic-BoldItalicCenturyGothic-ItalicCourierNewPS-BoldItalicMTCourierNewPS-BoldMTCourierNewPS-ItalicMTCourierNewPSMTGeorgiaGeorgia-BoldGeorgia-BoldItalicGeorgia-ItalicImpactLucidaConsoleTahomaTahoma-BoldTimesNewRomanMT-ExtraBoldTimesNewRomanPS-BoldItalicMTTimesNewRomanPS-BoldMTTimesNewRomanPS-ItalicMTTimesNewRomanPSMTTrebuchet-BoldItalicTrebuchetMSTrebuchetMS-BoldTrebuchetMS-ItalicVerdanaVerdana-BoldVerdana-BoldItalicVerdana-Italic]CannotEmbedFontPolicy WarningAutoPositionEPSFiles truePreserveOPIComments falseJPEG2000GrayACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtDSCReportingLevel 0PDFXOutputConditionIdentifier ()PDFXCompliantPDFOnly falseAllowTransparency falsePreserveCopyPage trueUsePrologue falseStartPage 1MonoImageDownsampleThreshold 10GrayImageDownsampleThreshold 10CheckCompliance [None]CreateJDFFile falsePDFXSetBleedBoxToMediaBox trueEmbedOpenType falseOPM 0PreserveOverprintSettings falseUCRandBGInfo RemoveColorImageDownsampleThreshold 10MonoImageDict ltltK -1gtgtGrayImageDownsampleType AverageDescription ltltENU (Use these settings to create Adobe PDF documents suitable for reliable viewing and printing of business documents Created PDF documents can be opened with Acrobat and Adobe Reader 50 and later)PTB 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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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Tab
le4(continu
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
answ
erkey
CEAquestionsan
dap
plica
tionofCEA
What
isth
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mag
nitudean
ddirection
ofindividual
stressor
effects
Xmdash
Only
based
onqualitativedata
Based
onrelative
contribution
ofstressor
toreducingam
ountof
estimated
suitab
lehab
itat
(req
uires
ldquobackfillingrdquo
individual
stressorsin
GISsee
text)
Pa
rameter
estimates
inmod
elrepresentmag
nitudedirection
and
certainty
ofstressor
effects
Stressor
effectstypically
are
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Relativeco
ntribution
ofstressors
toloss
ofsu
itab
lehab
itat
canbe
analysed
(req
uires
ldquoback-fillingrdquo
individual
stressorsin
GISsee
text)
What
isth
esh
apeofth
esp
eciesndashstressorresp
onse
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edNon
linea
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ships
(polyn
omial)canbetested
via
mod
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dread
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grap
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Sh
apeof
theresp
onse
canbe
estimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rce
ofinputmay
bequalitative
inform
ationor
empirical
mod
els)
Dostressors
combin
ead
ditivelyorin
tera
ctively
Xmdash
Only
viaqualitativedata
Xmdash
Not
read
ilyacco
mplish
edInteractionterm
scanbetested
forinclusion
viamod
elselection
procedures
Interactioneffectscanbeestimated
byusers
forinputinto
conditional
probab
ilitytables(sou
rceof
inputmay
bequalitativeinform
ationor
empirical
mod
els)
Coe
fficien
tsgraphscanbeusedto
catego
rize
synergisticve
rsus
antago
nisticeffects
What
isth
eove
rallim
pac
tofcu
mulative
effectson
populations
Xmdash
Only
based
onqualitative
data
Based
onloss
ofsu
itab
lehab
itat
Sp
eciesndashhab
itatndashstressor
mod
els
canbeusedto
predictan
dco
mpare
speciesab
undan
cemea
sures
amon
gldquon
o-disturban
cereferen
cerdquo
curren
tan
dfuture
landscap
eco
nditionsbased
onGISla
ndscap
esimulation
Po
pulation
resp
onseoutputcanbe
compared
amon
gldquon
o-disturban
ce
referencerdquocu
rren
tan
dfuture
scen
arioswithmod
elinput(hab
itat
stressor
amou
nts)b
ased
onGIS
landscap
esimulation
Sp
eciesndashhab
itat
mod
elprediction
sforhab
itat
amou
nts
orsp
ecies
abunda
nce
canbe
compa
redam
ong
ldquono-disturban
cereferen
cerdquocu
rren
tan
dfuture
landscapecondition
sba
sedon
GISla
ndscapesimulation
What
isth
eove
rallim
pac
tonth
eco
mmunity
Xmdash
Only
based
onqualitative
data
Req
uires
calculatingspeciesndashhab
itat
mod
elsforallspeciescalculating
predicted
abunda
ncesin
reference
curren
tor
future
landscapesand
comparingcommunitymetrics
amon
gthesepredictedcommun
ities
Withsu
fficien
tdata
community
metrics
canbeco
mpared
amon
glowh
ighstressor
catego
ries
Req
uires
calculatingpop
ulation
resp
onsesforsp
eciesin
reference
curren
tor
future
landscap
esa
nd
comparingco
mmunitymetrics
amon
gthesepredictedco
mmunities
Com
munitymetrics
cansimilarly
becompa
redusingspeciesabun
dance
inreference
curren
tor
future
landscap
esa
spredictedfrom
empiricalspeciesndashhab
itatndashstressor
mod
els
How
certainar
eresu
lts
How
cance
rtainty
be
assessed
Verylow
mdashSu
bjectivenature
mak
esitdifficu
ltto
quan
tify
certainty
Verylowmdash
Ifspeciesndashhab
itat
basedon
qualitativeinform
ation(subjective
nature
mak
esitdifficu
ltto
quan
tify
certainty)
Highmdash
Statisticalsignificance
ofstressor
effectscanbeestimated
viaco
nfiden
ceintervalsof
param
eter
estimates
Moderateto
highmdash
Dep
endson
extent
towhichmod
elsareba
sedon
qualitativeversusem
piricalda
ta
Mod
eratemdash
Ifspecies
habitat
relation
shipsbasedon
empiricald
ata
Approachallowsuncertainty
ofrelation
shipsto
bequan
tified
and
inputinto
themod
els
Ran
geofstressors
evaluated
Highmdash
Not
limited
bysp
atial
dataon
stressors
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
andaccessible
spatiald
ata
Low
tomoderatemdash
Limited
tostressorswithaccu
rate
and
accessible
spatiald
ata
Highmdash
Not
necessarily
limited
by
spatiald
ataon
stressorsifmod
els
based
onqualitativerelation
ships
208 Environ Rev Vol 29 2021
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In more recent and complex approaches empirical speciesndashhabitat models are developed by sampling terrestrial speciesacross habitat types in a study area and using advanced statisticalanalyses to quantify species associations with different habitatsModels are termed resource selection functions (RSF) when spe-cies data (presenceabsence usedunused) are analyzed at smallwithin-population scales and species distribution models (SDM)or species abundance models (SAM) when data (occurrence abun-dance respectively) are analyzed at large range-level scales (Guisanand Zimmermann 2000 Guisan and Thuiller 2005 Elith andLeathwick 2009 Hegel et al 2010) For terrestrial birds current andemerging methods can be followed to convert species counts at asample station (the most common sample metric for terrestrialbirds) to a density estimate (Soacutelymos et al 2013 Matsuoka et al2014) Reporting species responses as densities (versus relativeabundance) provides (i) a relevant and readily communicatedmeasure of species response (ii) a standardized measure acrossregions and studies and (iii) an evaluation against conservationor management targets or population viability estimatesA range of statistical modeling frameworks are available to analyse
speciesndashhabitat relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) Typically habitat coefficients in these modelsare used to assign habitat rankings for species Both expert andempirically based habitat rankings require validation againstexternal data (or internal validations in the case of empiricalmodels) to ensure their reliability for an area (Pearce and Ferrier2000 Boyce et al 2002 Muir et al 2011) Once validated modelresults are used to assess the amount and distribution of suitablehabitat for species within a GIS (Millspaugh and Thompson 2009)Additionally when species responses have been converted to den-sity estimates coefficients can be coupled with GIS maps to esti-mate species densities or population sizes Within the context ofCEA the footprint or area of proposed projects existing projectsand disturbances and future land use scenarios can be overlaidon these maps to estimate the change in suitable habitat andassess effects on terrestrial species based on these habitat changeswithin the study area
322 Examples of applicationsWe highlight both project-based CEA and effects-based CEA
examples to demonstrate the flexibility of the habitat supplymethod Project-based CEAs typically use literature-based or hab-itat suitability models to identify map and quantify the area ofsuitable habitat for a given species within the study area We pro-vide a hypothetical example to explain how the habitat supplymethod is typically used to assess project-specific effects (Table 6)Changes in the amount of suitable habitat during the project(application phase eg from 10 to 6 of study area in Table 6)and after the project (closureremediation phase eg 0 changein Table 6) are estimated relative to current or baseline The hab-itat supply method is not typically used to assess CE because theextent to which existing or past stressors have affected pre-existinghabitat types is often not known Instead CE requirements areoften met by comparing the total area of the proposed project (eg5 of study area in Table 6) to the total area of other stressors regard-less of habitat affected (eg 35 Table 6) Thus in high or moder-ately disturbed areas these comparisons may suggest that a projectwould have minimal added effects relative to existing stressorsWhere data and resources allow some project-based CEAs mayuse historic maps or create hypothetical no-disturbance land-scapes within a GIS (eg see methods in ABMI 2017 Mahon et al2019) to assess how other stressors may have affected specific habi-tats However resource requirements (time data technical exper-tise) place this type of assessment beyond themandate or capacity ofmost project-based CEAs We include an example of the habitat sup-ply method from a recent project-based CEA from the IAAC RegistryT
able
4(conclud
ed)
Qualitativereview
Hab
itat
supply
Empirical
species-stressor
mod
els
Decisionsu
pportmod
els(Bay
esianbelief
networks)
Abilityto
inco
rpora
tetrad
itional
knowledge
Yes
Notusu
ally
Notusu
ally
Yes
Statistica
ltech
nical
expertise
required
Low
tomoderatemdash
Mod
eratewith
form
almod
eling
Moderate
High
Highto
very
high(specialized)
Tim
efra
me
Month
s
Month
sto
ltyea
rmdash
For
speciesndashhab
itat
mod
elsbased
onqualitativedata(dep
enden
ton
statusof
spatiald
ata
detailo
fthe
mod
ela
vailab
ilityof
species
inform
ation)
gtYea
rto
multi-ye
ar
Speciesdataco
llection
may
nee
dto
occu
rov
ergt1ye
arto
provide
sufficien
tsamples
Month
sto
gtmulti-ye
armdash
Highly
dep
enden
ton
sourceof
datainput
(existingdata
new
empirical
data
collection
)
Statisticalm
odel
dev
elop
men
tan
drefinem
entcanbetime
consu
ming
Modelingap
proachmay
require
additional
wee
ksor
month
s
gtYea
rmdash
Forem
piricalspe
ciesndashh
abitat
mod
elsthat
requ
irenew
data
collection
Dep
enden
ton
timerequired
toco
mpile
evaluatean
dcatego
rize
hab
itat
andstressor
data
Mahon and Pelech 209
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se o
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Tab
le5
Exa
mple
ofaqualitativereview
(matrixtable)for
aproject-based
CEA
Valued
compon
ent
Potential
effects
Mitigation
compen
sation
Residual
effect
Sign
ifican
ceof
residual
effects
Eco
logical
societal
value
Ove
rallsign
ifican
ceMag
nitude
Spatiale
xten
t
Freq
uen
cy
probab
ility
Duration
Rev
ersibility
Graywolf
Assoc
iatedwith
major
preysp
ecies
(moo
sed
eer)A
t
closure
increa
sed
area
sof
grasslan
d
willc
reatewhite-
tailed
dee
rhab
itat
andpoten
tially
higher
dee
ran
d
wolfnumbers
Limitfootprintof
disturbed
area
s
Increase
indeeran
d
wolfp
opulation
s
over
timeResidual
adverseeffect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
highqualitymoo
se
hab
itattherefore
limited
wolf
pop
ulation
MIN
IMAL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
is
long-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
is
reversibledu
ring
site
reclam
ation
Moo
sefollow
edby
wolvessh
ould
return
tosite
at
curren
tlevels
LOW
Wolvesarecommon
inthelocaland
region
alstudy
areasLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
wolve
s
aremob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
American
black
bea
r
Initialm
igration
duringsite
preparationan
d
constru
ction
return
tosite
area
afterperiodof
human
noise
hab
ituation
Limitfootprintof
disturbed
areas
Establishwildlife
policyto
minim
ize
human
interaction
withwildlifean
d
decrease
poten
tial
forhab
ituation
Increa
sedseason
al
use
ofthesite
over
timedep
endson
succession
of
reclaimed
area
s
Residual
adve
rse
effect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
abov
eav
erag
ebea
r
hab
itatM
INIM
AL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
islong-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
reversibledu
ring
site
reclam
ation
New
grasslan
d
hab
itat
may
increase
the
season
alhab
itat
use
bybe
ars
LOW
Bearde
nsity
inthe
study
site
islikely
comparab
leto
that
inthesurrou
nding
landscapeLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
bea
rsare
mob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
NoteT
hetable
summarizes
theproposed
project
effectsmitigationan
dco
mpen
sation
mea
surespoten
tial
residual
effects(theen
vironmen
tale
ffectof
aproject
that
remainsaftermitigationmea
sureshav
ebee
nim
plemen
ted)sign
ificance
ofresidual
effectsin
considerationof
ava
rietyof
assessmen
tfactors(ratings
inboldface
type)a
ndwhether
asign
ificantad
verseen
vironmen
tale
ffecton
ava
lued
compon
entisex
pected
asaresu
ltof
project
implemen
tation
Sou
rceEnvironmen
talImpactStatem
entfortheMarathon
PGM-CuProject20
12Thetable
represents
description
san
dratings
summarizingtheen
vironmen
tale
ffects
ofametal
minedev
elop
men
ton
twova
lued
compon
ents
(graywolfan
dAmerican
black
bea
r)
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(Environmental Impact Statement for the Springbank Off-streamReservoir Project 2018) to estimate area changes in the amountof suitable habitat (based on habitat suitability models) for selectVCs Spraguersquos Pipit (Anthus spragueii) and Northern Leopard Frog(Lithobates pipiens) during different flood scenarios created by waterdiversion to a channel and reservoir within the proposed projectarea (Table 7)Effects-based CEAs typically involve the creation and application
of empirical speciesndashhabitat models within the study area Maceet al (1999) evaluated the CE of human and resource activity ongrizzly bear (Ursus arctos) habitat inwesternMontana during springsummer and fall seasons The authors modelled bear resourceselection from telemetry data satellite imagery elevation humanactivity points roads and trails and extrapolatedmodels to a largerregion with similar climate and vegetation Quantifying and map-ping areas with a high probability of use by bears in each seasonwas used to inform conservation and management actions (egroad or trail closures within important seasonal habitats habitatrestoration future road creation) Roumldder et al (2016) outline aninnovative approach to integrate SDM and potential connectivitymodels to identify connected and isolated populations and habitatcorridors for a species of conservation concern the Sand Lizard(Lacerta agilis) in western Germany (Fig 2) Quantifying potentialconnectivity areas (for movement like dispersal or migration) wasused to identify areas of strong connectivity persistent isolationand stable connective networks needed to connect populationsThis proposed framework may be particularly useful for proposeddevelopments that affect (i) portions of a previously larger inter-connected population or (ii) connective features between perma-nently colonized habitat patches Additional examples from thepublished literature are listed in Table 3
323 Advantages and limitations CEA questions and whenwhyto use3231 AdvantagesAdvantages of the habitat supply method include its ability to
quantify effects using loss of suitable habitat This approach
which has wide-spread application in wildlife research may besuitable when (i) data to map and measure habitat change areavailable but empirical data on species response to specific stres-sors are limited (ii) collaborative approaches focused on localissues and values are important (eg speciesndashhabitat associa-tions may be based on both qualitative (TK local and expertknowledge) and quantitative (speciesndashhabitat models) data) and(iii) data to develop empirical speciesndashhabitat models and adjusteddensity estimates are available so that total impacts can be com-pared to current and future population targets (eg Mahon et al2014)
3232 LimitationsLimitations to the habitat supply modelling approach include
the availability of accurate publicly available spatial data forhabitats and stressors and reliable specieshabitat associationsfrom the region of interest If additional data are required torefine existing habitat suitability models or create new empiricalspeciesndashhabitat models within a study area stakeholders or part-ners should a priori develop rigorous sampling designs and sur-vey protocols to meet project objectives We note two additionalcautions when using this approach Specieshabitat associationsbased on limited empirical data from within the region or datafrom outside the region require model validation and testing(Muir et al 2011) Finally stressor effects are limited to how stres-sors contribute to the loss of suitable habitat (habitat changesare used to infer population consequences) Habitat-based analy-sis alone may not be useful because (i) habitat is assumed to bea useful measure of population status (ii) habitat needs of the tar-get species or VC are assumed to be known (Schultz 2010) and(iii) specific stressor effects on the target species or VC are notquantified (eg the shape magnitude and direction of speciesrsquoresponse to stressors indirect or novel effects of stressors on speciesor interactions among stressors) Failure to identify which stressorsare influencing target species or VCsmay limit future research intobiological mechanisms (eg habitat selection movement disper-sal reproduction survival predatorndashprey dynamics) needed to
Fig 1 Example of a qualitative review (conceptual modeldiagram) for an effects-based CEA Figure represents an annual life cycle ofevents of Greater Sage-Grouse responding to oil and gas development in southwest Wyoming USA (from Heinrichs et al 2019 reproducedwith permission of Ecol Appl Vol 29 copy2019 Ecological Society of America)
Mahon and Pelech 211
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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
212 Environ Rev Vol 29 2021
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
Mahon and Pelech 213
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
vegetation layer including ldquobackfilledrdquo vegetation in human footprints(Version 6) Alberta Biodiversity Monitoring Institute Edmonton Reportavailable from httpsabmicahomehtml [last accessed 13 November 2018]
Austin M 2007 Species distribution models and ecological theory a criticalassessment and some possible new approaches Ecol Modell 200 1ndash19 doi101016jecolmodel200607005
Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
BC Resource Inventory Committee 1999 British Columbia Wildlife HabitatRating Standards Available from httpswww2govbccaassetsgovenvironmentnatural-resource-stewardshipnr-laws-policyriscwhrspdf [last accessed13 November 2018]
Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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r pe
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se o
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Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
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Canadian Environmental Assessment Agency (CEAA) Expert Panel Review 2017Building common ground a new vision for impact assessment in Canada Thefinal report of the expert panel for the review of Environmental assessmentprocesses Available from httpswwwcanadacaenservicesenvironmentconservationassessmentsenvironmental-reviewsenvironmental-assessment-processesbuilding-common-groundhtml [last accessed 10 April 2020]
Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
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Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
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Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
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Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
Noble B Liu J and Hackett P 2017 The contribution of project environmentalassessment to assessing and managing cumulative effects individually andcollectively insignificant Environ Manage 59 531ndash545 doi101007s00267-016-0799-7 PMID27885387
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Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
Salice CJ 2012 Multiple stressors and amphibians Contributions of adversehealth effects and altered hydroperiod to population decline and extinctionJ Herpetol 46(4) 675ndash681 doi10167011-091
Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
Sawyer H Kauffman MJ and Nielson RM 2009 Influence of well pad ac-tivity on winter habitat selection patterns of Mule Deer J Wildl Manage73(7) 1052ndash1061 doi1021932008-478
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Silva JP Santos M Queiroacutes L Leitatildeo D Moreira F Pinto M et al2010 Estimating the influence of overhead transmission power lies andlandscape context on the density of little bustard Tetrax tetrax breedingpopulations Ecol Modell 221 1954ndash1963 doi101016jecolmodel201003027
Mahon and Pelech 223
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Smit B and Spaling H 1995 Methods for cumulative effects assessmentEnviron Impact Assess Rev 15 81ndash106 doi1010160195-9255(94)00027-X
Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
Steventon JD Sutherland GD and Arcese P 2006 A population-viability-based risk assessment of Marbled Murrelet nesting habitat policy in BritishColumbia Can J For Res 36(12) 3075ndash3086 doi101139x06-198
Stralberg D Wang X Parisien MA Robinne FN Soacutelymos P Mahon CLet al 2018 Wildfire-mediated vegetation change in boreal forests of AlbertaCanada Ecosphere 9 e02156 doi101002ecs22156
Sutherland GD Waterhouse FL Smith J Saunders SC Paige K andMalt J 2016 Developing a systematic simulation-based approach forselecting indicators in strategic cumulative effects assessments with mul-tiple environmental valued components Ecol Indic 61 512ndash525 doi101016jecolind201510004
Swor T and Canter L 2011 Promoting environmental sustainability via anexpert elicitation process Environ Impact Assess Rev 31 506ndash514 doi101016jeiar201101014
Tremblay JA Boulanger Y Cyr D Taylor AR Price DT and St-Laurent MH2018 Harvesting interacts with climate change to affect future habitat qualityof a focal species in eastern Canadarsquos boreal forest PLoS ONE 13 e0191645doi101371journalpone0191645 PMID29414989
US Fish and Wildlife Service (USFWS) 1980 Habitat as a basis for environ-mental assessment 101 ESM US Department of the Interior Fish andWildlife Service Washington DC
US Fish and Wildlife Service (USFWS) 1981 Standards for development ofhabitat suitability models 103 ESM US Department of the Interior Fishand Wildlife Service Washington DC
Usher PJ 2000 Traditional ecological knowledge in environmental assess-ment and management Arctic 53 183ndash193 doi1014430arctic849
Uusitalo L 2007 Advantages and challenges of Bayesian networks in environ-mental modelling Ecol Modell 203 312ndash318 doi101016jecolmodel200611033
Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
Westwood AR Olszynski M Fox CH Ford AT Jacob AL Moore JWand Palen WJ 2019 The role of science in contemporary Canadian envi-ronmental decision making the example of environmental assessmentUniv Br Columbia Law Rev 52(1) 243ndash291
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224 Environ Rev Vol 29 2021
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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtDSCReportingLevel 0PDFXOutputConditionIdentifier ()PDFXCompliantPDFOnly falseAllowTransparency falsePreserveCopyPage trueUsePrologue falseStartPage 1MonoImageDownsampleThreshold 10GrayImageDownsampleThreshold 10CheckCompliance [None]CreateJDFFile falsePDFXSetBleedBoxToMediaBox trueEmbedOpenType falseOPM 0PreserveOverprintSettings falseUCRandBGInfo RemoveColorImageDownsampleThreshold 10MonoImageDict ltltK -1gtgtGrayImageDownsampleType AverageDescription ltltENU (Use these settings to create Adobe PDF documents suitable for reliable viewing and printing of business documents Created PDF documents can be opened with Acrobat and Adobe Reader 50 and later)PTB 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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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In more recent and complex approaches empirical speciesndashhabitat models are developed by sampling terrestrial speciesacross habitat types in a study area and using advanced statisticalanalyses to quantify species associations with different habitatsModels are termed resource selection functions (RSF) when spe-cies data (presenceabsence usedunused) are analyzed at smallwithin-population scales and species distribution models (SDM)or species abundance models (SAM) when data (occurrence abun-dance respectively) are analyzed at large range-level scales (Guisanand Zimmermann 2000 Guisan and Thuiller 2005 Elith andLeathwick 2009 Hegel et al 2010) For terrestrial birds current andemerging methods can be followed to convert species counts at asample station (the most common sample metric for terrestrialbirds) to a density estimate (Soacutelymos et al 2013 Matsuoka et al2014) Reporting species responses as densities (versus relativeabundance) provides (i) a relevant and readily communicatedmeasure of species response (ii) a standardized measure acrossregions and studies and (iii) an evaluation against conservationor management targets or population viability estimatesA range of statistical modeling frameworks are available to analyse
speciesndashhabitat relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) Typically habitat coefficients in these modelsare used to assign habitat rankings for species Both expert andempirically based habitat rankings require validation againstexternal data (or internal validations in the case of empiricalmodels) to ensure their reliability for an area (Pearce and Ferrier2000 Boyce et al 2002 Muir et al 2011) Once validated modelresults are used to assess the amount and distribution of suitablehabitat for species within a GIS (Millspaugh and Thompson 2009)Additionally when species responses have been converted to den-sity estimates coefficients can be coupled with GIS maps to esti-mate species densities or population sizes Within the context ofCEA the footprint or area of proposed projects existing projectsand disturbances and future land use scenarios can be overlaidon these maps to estimate the change in suitable habitat andassess effects on terrestrial species based on these habitat changeswithin the study area
322 Examples of applicationsWe highlight both project-based CEA and effects-based CEA
examples to demonstrate the flexibility of the habitat supplymethod Project-based CEAs typically use literature-based or hab-itat suitability models to identify map and quantify the area ofsuitable habitat for a given species within the study area We pro-vide a hypothetical example to explain how the habitat supplymethod is typically used to assess project-specific effects (Table 6)Changes in the amount of suitable habitat during the project(application phase eg from 10 to 6 of study area in Table 6)and after the project (closureremediation phase eg 0 changein Table 6) are estimated relative to current or baseline The hab-itat supply method is not typically used to assess CE because theextent to which existing or past stressors have affected pre-existinghabitat types is often not known Instead CE requirements areoften met by comparing the total area of the proposed project (eg5 of study area in Table 6) to the total area of other stressors regard-less of habitat affected (eg 35 Table 6) Thus in high or moder-ately disturbed areas these comparisons may suggest that a projectwould have minimal added effects relative to existing stressorsWhere data and resources allow some project-based CEAs mayuse historic maps or create hypothetical no-disturbance land-scapes within a GIS (eg see methods in ABMI 2017 Mahon et al2019) to assess how other stressors may have affected specific habi-tats However resource requirements (time data technical exper-tise) place this type of assessment beyond themandate or capacity ofmost project-based CEAs We include an example of the habitat sup-ply method from a recent project-based CEA from the IAAC RegistryT
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ituation
Increa
sedseason
al
use
ofthesite
over
timedep
endson
succession
of
reclaimed
area
s
Residual
adve
rse
effect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
abov
eav
erag
ebea
r
hab
itatM
INIM
AL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
islong-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
reversibledu
ring
site
reclam
ation
New
grasslan
d
hab
itat
may
increase
the
season
alhab
itat
use
bybe
ars
LOW
Bearde
nsity
inthe
study
site
islikely
comparab
leto
that
inthesurrou
nding
landscapeLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
bea
rsare
mob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
NoteT
hetable
summarizes
theproposed
project
effectsmitigationan
dco
mpen
sation
mea
surespoten
tial
residual
effects(theen
vironmen
tale
ffectof
aproject
that
remainsaftermitigationmea
sureshav
ebee
nim
plemen
ted)sign
ificance
ofresidual
effectsin
considerationof
ava
rietyof
assessmen
tfactors(ratings
inboldface
type)a
ndwhether
asign
ificantad
verseen
vironmen
tale
ffecton
ava
lued
compon
entisex
pected
asaresu
ltof
project
implemen
tation
Sou
rceEnvironmen
talImpactStatem
entfortheMarathon
PGM-CuProject20
12Thetable
represents
description
san
dratings
summarizingtheen
vironmen
tale
ffects
ofametal
minedev
elop
men
ton
twova
lued
compon
ents
(graywolfan
dAmerican
black
bea
r)
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(Environmental Impact Statement for the Springbank Off-streamReservoir Project 2018) to estimate area changes in the amountof suitable habitat (based on habitat suitability models) for selectVCs Spraguersquos Pipit (Anthus spragueii) and Northern Leopard Frog(Lithobates pipiens) during different flood scenarios created by waterdiversion to a channel and reservoir within the proposed projectarea (Table 7)Effects-based CEAs typically involve the creation and application
of empirical speciesndashhabitat models within the study area Maceet al (1999) evaluated the CE of human and resource activity ongrizzly bear (Ursus arctos) habitat inwesternMontana during springsummer and fall seasons The authors modelled bear resourceselection from telemetry data satellite imagery elevation humanactivity points roads and trails and extrapolatedmodels to a largerregion with similar climate and vegetation Quantifying and map-ping areas with a high probability of use by bears in each seasonwas used to inform conservation and management actions (egroad or trail closures within important seasonal habitats habitatrestoration future road creation) Roumldder et al (2016) outline aninnovative approach to integrate SDM and potential connectivitymodels to identify connected and isolated populations and habitatcorridors for a species of conservation concern the Sand Lizard(Lacerta agilis) in western Germany (Fig 2) Quantifying potentialconnectivity areas (for movement like dispersal or migration) wasused to identify areas of strong connectivity persistent isolationand stable connective networks needed to connect populationsThis proposed framework may be particularly useful for proposeddevelopments that affect (i) portions of a previously larger inter-connected population or (ii) connective features between perma-nently colonized habitat patches Additional examples from thepublished literature are listed in Table 3
323 Advantages and limitations CEA questions and whenwhyto use3231 AdvantagesAdvantages of the habitat supply method include its ability to
quantify effects using loss of suitable habitat This approach
which has wide-spread application in wildlife research may besuitable when (i) data to map and measure habitat change areavailable but empirical data on species response to specific stres-sors are limited (ii) collaborative approaches focused on localissues and values are important (eg speciesndashhabitat associa-tions may be based on both qualitative (TK local and expertknowledge) and quantitative (speciesndashhabitat models) data) and(iii) data to develop empirical speciesndashhabitat models and adjusteddensity estimates are available so that total impacts can be com-pared to current and future population targets (eg Mahon et al2014)
3232 LimitationsLimitations to the habitat supply modelling approach include
the availability of accurate publicly available spatial data forhabitats and stressors and reliable specieshabitat associationsfrom the region of interest If additional data are required torefine existing habitat suitability models or create new empiricalspeciesndashhabitat models within a study area stakeholders or part-ners should a priori develop rigorous sampling designs and sur-vey protocols to meet project objectives We note two additionalcautions when using this approach Specieshabitat associationsbased on limited empirical data from within the region or datafrom outside the region require model validation and testing(Muir et al 2011) Finally stressor effects are limited to how stres-sors contribute to the loss of suitable habitat (habitat changesare used to infer population consequences) Habitat-based analy-sis alone may not be useful because (i) habitat is assumed to bea useful measure of population status (ii) habitat needs of the tar-get species or VC are assumed to be known (Schultz 2010) and(iii) specific stressor effects on the target species or VC are notquantified (eg the shape magnitude and direction of speciesrsquoresponse to stressors indirect or novel effects of stressors on speciesor interactions among stressors) Failure to identify which stressorsare influencing target species or VCsmay limit future research intobiological mechanisms (eg habitat selection movement disper-sal reproduction survival predatorndashprey dynamics) needed to
Fig 1 Example of a qualitative review (conceptual modeldiagram) for an effects-based CEA Figure represents an annual life cycle ofevents of Greater Sage-Grouse responding to oil and gas development in southwest Wyoming USA (from Heinrichs et al 2019 reproducedwith permission of Ecol Appl Vol 29 copy2019 Ecological Society of America)
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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
218 Environ Rev Vol 29 2021
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
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tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
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Austin M 2007 Species distribution models and ecological theory a criticalassessment and some possible new approaches Ecol Modell 200 1ndash19 doi101016jecolmodel200607005
Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
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Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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iron
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n 12
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r pe
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se o
nly
Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
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Canadian Environmental Assessment Agency (CEAA) Expert Panel Review 2017Building common ground a new vision for impact assessment in Canada Thefinal report of the expert panel for the review of Environmental assessmentprocesses Available from httpswwwcanadacaenservicesenvironmentconservationassessmentsenvironmental-reviewsenvironmental-assessment-processesbuilding-common-groundhtml [last accessed 10 April 2020]
Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
Environmental Impact Statement for the Springbank Off-stream ReservoirProject 2018 Environmental Impact Assessment of the Springbank Off-stream Reservoir Project Stantec Incorporated Available from httpsiaac-aeicgcca [last accessed 15 June 2020]
Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
Impact Assessment Agency of Canada (IAAC) 2019 The Impact AssessmentProcess Timelines and Outputs Available from httpswwwcanadacaenimpact-assessment-agencyhtml [last accessed 20 August 2020]
Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
Johnson C 2016 Understanding the spatial responses of caribou to human-caused disturbance In Development of modeling tools to address cumulativeeffects on the summer range of the Bathurst caribou herd ndash a demonstrationproject Edited by JS Nishi and A Gunn Environment and Natural ResourcesGovernment of the Northwest Territories Yellowknife NT pp 13ndash36
Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
Millspaugh JJ and Thompson FR III 2009 Models for planning wildlifeconservation in large landscapes Elsevier Amsterdam the Netherlands
Morrison ML Marcot BG and Mannan RW 2006 Wildlife-habitat rela-tionships concepts and applications 3rd ed Island Press WashingtonDC USA
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
Noble B Liu J and Hackett P 2017 The contribution of project environmentalassessment to assessing and managing cumulative effects individually andcollectively insignificant Environ Manage 59 531ndash545 doi101007s00267-016-0799-7 PMID27885387
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Pearce J and Ferrier S 2000 Evaluating the predictive performance ofhabitat models developed using logistic regression Ecol Modell 133(3)225ndash245 doi101016S0304-3800(00)00322-7
Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
Salice CJ 2012 Multiple stressors and amphibians Contributions of adversehealth effects and altered hydroperiod to population decline and extinctionJ Herpetol 46(4) 675ndash681 doi10167011-091
Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
Sawyer H Kauffman MJ and Nielson RM 2009 Influence of well pad ac-tivity on winter habitat selection patterns of Mule Deer J Wildl Manage73(7) 1052ndash1061 doi1021932008-478
Schieck J Muhly T Huggard D Solymos P Pan D Heckbert S andBayne E 2014 Predicting the cumulative effects of human developmenton biodiversity in northeastern Alberta Petroleum Technology AllianceCanada (Alberta Upstream Petroleum Research Fund) Calgary AB Avail-able from httpsauprfptacorgecological-2a-tool-to-assess-cumulative-effects-of-development-on-biodiversity [last accessed 13 November 2018]
Schultz C 2010 Challenges in connecting cumulative effects analysis toeffective wildlife conservation planning BioScience 60(7) 545ndash551 doi101525bio201060710
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Silva JP Santos M Queiroacutes L Leitatildeo D Moreira F Pinto M et al2010 Estimating the influence of overhead transmission power lies andlandscape context on the density of little bustard Tetrax tetrax breedingpopulations Ecol Modell 221 1954ndash1963 doi101016jecolmodel201003027
Mahon and Pelech 223
Published by NRC Research Press
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Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
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Swor T and Canter L 2011 Promoting environmental sustainability via anexpert elicitation process Environ Impact Assess Rev 31 506ndash514 doi101016jeiar201101014
Tremblay JA Boulanger Y Cyr D Taylor AR Price DT and St-Laurent MH2018 Harvesting interacts with climate change to affect future habitat qualityof a focal species in eastern Canadarsquos boreal forest PLoS ONE 13 e0191645doi101371journalpone0191645 PMID29414989
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US Fish and Wildlife Service (USFWS) 1981 Standards for development ofhabitat suitability models 103 ESM US Department of the Interior Fishand Wildlife Service Washington DC
Usher PJ 2000 Traditional ecological knowledge in environmental assess-ment and management Arctic 53 183ndash193 doi1014430arctic849
Uusitalo L 2007 Advantages and challenges of Bayesian networks in environ-mental modelling Ecol Modell 203 312ndash318 doi101016jecolmodel200611033
Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
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224 Environ Rev Vol 29 2021
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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtDSCReportingLevel 0PDFXOutputConditionIdentifier ()PDFXCompliantPDFOnly falseAllowTransparency falsePreserveCopyPage trueUsePrologue falseStartPage 1MonoImageDownsampleThreshold 10GrayImageDownsampleThreshold 10CheckCompliance [None]CreateJDFFile falsePDFXSetBleedBoxToMediaBox trueEmbedOpenType falseOPM 0PreserveOverprintSettings falseUCRandBGInfo RemoveColorImageDownsampleThreshold 10MonoImageDict ltltK -1gtgtGrayImageDownsampleType AverageDescription ltltENU (Use these settings to create Adobe PDF documents suitable for reliable viewing and printing of business documents Created PDF documents can be opened with Acrobat and Adobe Reader 50 and later)PTB 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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltFEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020be44c988b2c8c2a40020bb38c11cb97c0020c548c815c801c73cb85c0020bcf4ace00020c778c1c4d558b2940020b3700020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002egtNLD (Gebruik deze instellingen om Adobe PDF-documenten te maken waarmee zakelijke documenten betrouwbaar kunnen worden weergegeven en afgedrukt De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 50 en hoger)NOR 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 (Utilizzare queste impostazioni per creare documenti Adobe PDF adatti per visualizzare e stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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 ltFEFF4f7f75288fd94e9b8bbe5b9a521b5efa7684002000410064006f006200650020005000440046002065876863900275284e8e55464e1a65876863768467e5770b548c62535370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c676562535f00521b5efa768400200050004400460020658768633002gtSUO 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 ltFEFF4f7f752890194e9b8a2d7f6e5efa7acb7684002000410064006f006200650020005000440046002065874ef69069752865bc666e901a554652d965874ef6768467e5770b548c52175370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c4f86958b555f5df25efa7acb76840020005000440046002065874ef63002gtgtgtCropMonoImages trueDefaultRenderingIntent RelativeColorimetericPreserveHalftoneInfo falseColorImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtCropGrayImages truePDFXOutputCondition ()SubsetFonts trueEncodeMonoImages trueCropColorImages truePDFXNoTrimBoxError truegtgtsetdistillerparamsltltPageSize [61207920]HWResolution [600600]gtgtsetpagedevice
Tab
le5
Exa
mple
ofaqualitativereview
(matrixtable)for
aproject-based
CEA
Valued
compon
ent
Potential
effects
Mitigation
compen
sation
Residual
effect
Sign
ifican
ceof
residual
effects
Eco
logical
societal
value
Ove
rallsign
ifican
ceMag
nitude
Spatiale
xten
t
Freq
uen
cy
probab
ility
Duration
Rev
ersibility
Graywolf
Assoc
iatedwith
major
preysp
ecies
(moo
sed
eer)A
t
closure
increa
sed
area
sof
grasslan
d
willc
reatewhite-
tailed
dee
rhab
itat
andpoten
tially
higher
dee
ran
d
wolfnumbers
Limitfootprintof
disturbed
area
s
Increase
indeeran
d
wolfp
opulation
s
over
timeResidual
adverseeffect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
highqualitymoo
se
hab
itattherefore
limited
wolf
pop
ulation
MIN
IMAL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
is
long-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
is
reversibledu
ring
site
reclam
ation
Moo
sefollow
edby
wolvessh
ould
return
tosite
at
curren
tlevels
LOW
Wolvesarecommon
inthelocaland
region
alstudy
areasLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
wolve
s
aremob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
American
black
bea
r
Initialm
igration
duringsite
preparationan
d
constru
ction
return
tosite
area
afterperiodof
human
noise
hab
ituation
Limitfootprintof
disturbed
areas
Establishwildlife
policyto
minim
ize
human
interaction
withwildlifean
d
decrease
poten
tial
forhab
ituation
Increa
sedseason
al
use
ofthesite
over
timedep
endson
succession
of
reclaimed
area
s
Residual
adve
rse
effect
assessed
for
sign
ificance
Studyarea
doe
snot
appea
rto
provide
abov
eav
erag
ebea
r
hab
itatM
INIM
AL
Loss
ofhab
itat
limited
tosite
studyarea
MIN
IMAL
Occurs
oneor
more
timesb
uteffect
islong-lasting
MEDIU
M
Loss
occu
rsearlyin
theproject
andis
limited
tothe
phases
ofthe
projectM
EDIU
M
Loss
ofhab
itat
reversibledu
ring
site
reclam
ation
New
grasslan
d
hab
itat
may
increase
the
season
alhab
itat
use
bybe
ars
LOW
Bearde
nsity
inthe
study
site
islikely
comparab
leto
that
inthesurrou
nding
landscapeLO
W
Nosign
ificantad
verse
effectLossof
hab
itat
andpop
ulation
will
belimited
tothesite
studyarea
bea
rsare
mob
ilean
dwill
return
afterproject
closureE
ffects
are
reve
rsible
through
reclam
ationan
d
hab
itat
restoration
NoteT
hetable
summarizes
theproposed
project
effectsmitigationan
dco
mpen
sation
mea
surespoten
tial
residual
effects(theen
vironmen
tale
ffectof
aproject
that
remainsaftermitigationmea
sureshav
ebee
nim
plemen
ted)sign
ificance
ofresidual
effectsin
considerationof
ava
rietyof
assessmen
tfactors(ratings
inboldface
type)a
ndwhether
asign
ificantad
verseen
vironmen
tale
ffecton
ava
lued
compon
entisex
pected
asaresu
ltof
project
implemen
tation
Sou
rceEnvironmen
talImpactStatem
entfortheMarathon
PGM-CuProject20
12Thetable
represents
description
san
dratings
summarizingtheen
vironmen
tale
ffects
ofametal
minedev
elop
men
ton
twova
lued
compon
ents
(graywolfan
dAmerican
black
bea
r)
210 Environ Rev Vol 29 2021
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(Environmental Impact Statement for the Springbank Off-streamReservoir Project 2018) to estimate area changes in the amountof suitable habitat (based on habitat suitability models) for selectVCs Spraguersquos Pipit (Anthus spragueii) and Northern Leopard Frog(Lithobates pipiens) during different flood scenarios created by waterdiversion to a channel and reservoir within the proposed projectarea (Table 7)Effects-based CEAs typically involve the creation and application
of empirical speciesndashhabitat models within the study area Maceet al (1999) evaluated the CE of human and resource activity ongrizzly bear (Ursus arctos) habitat inwesternMontana during springsummer and fall seasons The authors modelled bear resourceselection from telemetry data satellite imagery elevation humanactivity points roads and trails and extrapolatedmodels to a largerregion with similar climate and vegetation Quantifying and map-ping areas with a high probability of use by bears in each seasonwas used to inform conservation and management actions (egroad or trail closures within important seasonal habitats habitatrestoration future road creation) Roumldder et al (2016) outline aninnovative approach to integrate SDM and potential connectivitymodels to identify connected and isolated populations and habitatcorridors for a species of conservation concern the Sand Lizard(Lacerta agilis) in western Germany (Fig 2) Quantifying potentialconnectivity areas (for movement like dispersal or migration) wasused to identify areas of strong connectivity persistent isolationand stable connective networks needed to connect populationsThis proposed framework may be particularly useful for proposeddevelopments that affect (i) portions of a previously larger inter-connected population or (ii) connective features between perma-nently colonized habitat patches Additional examples from thepublished literature are listed in Table 3
323 Advantages and limitations CEA questions and whenwhyto use3231 AdvantagesAdvantages of the habitat supply method include its ability to
quantify effects using loss of suitable habitat This approach
which has wide-spread application in wildlife research may besuitable when (i) data to map and measure habitat change areavailable but empirical data on species response to specific stres-sors are limited (ii) collaborative approaches focused on localissues and values are important (eg speciesndashhabitat associa-tions may be based on both qualitative (TK local and expertknowledge) and quantitative (speciesndashhabitat models) data) and(iii) data to develop empirical speciesndashhabitat models and adjusteddensity estimates are available so that total impacts can be com-pared to current and future population targets (eg Mahon et al2014)
3232 LimitationsLimitations to the habitat supply modelling approach include
the availability of accurate publicly available spatial data forhabitats and stressors and reliable specieshabitat associationsfrom the region of interest If additional data are required torefine existing habitat suitability models or create new empiricalspeciesndashhabitat models within a study area stakeholders or part-ners should a priori develop rigorous sampling designs and sur-vey protocols to meet project objectives We note two additionalcautions when using this approach Specieshabitat associationsbased on limited empirical data from within the region or datafrom outside the region require model validation and testing(Muir et al 2011) Finally stressor effects are limited to how stres-sors contribute to the loss of suitable habitat (habitat changesare used to infer population consequences) Habitat-based analy-sis alone may not be useful because (i) habitat is assumed to bea useful measure of population status (ii) habitat needs of the tar-get species or VC are assumed to be known (Schultz 2010) and(iii) specific stressor effects on the target species or VC are notquantified (eg the shape magnitude and direction of speciesrsquoresponse to stressors indirect or novel effects of stressors on speciesor interactions among stressors) Failure to identify which stressorsare influencing target species or VCsmay limit future research intobiological mechanisms (eg habitat selection movement disper-sal reproduction survival predatorndashprey dynamics) needed to
Fig 1 Example of a qualitative review (conceptual modeldiagram) for an effects-based CEA Figure represents an annual life cycle ofevents of Greater Sage-Grouse responding to oil and gas development in southwest Wyoming USA (from Heinrichs et al 2019 reproducedwith permission of Ecol Appl Vol 29 copy2019 Ecological Society of America)
Mahon and Pelech 211
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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
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nly
indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
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Mahon and Pelech 221
Published by NRC Research Press
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iron
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n 12
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Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
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Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
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222 Environ Rev Vol 29 2021
Published by NRC Research Press
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r pe
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Wilson RR Liebezeit JR and Loya WM 2013 Accounting for uncer-tainty in oil and gas development impacts to wildlife in Alaska ConservLett 6 350ndash358 doi101111conl12016
Wintle BA Bekessy SA Venier LA Pearce JL and Chisholm RA 2005Utility of dynamic-landscape metapopulation models for sustainable forestmanagement Conserv Biol 19 1930ndash1943 doi101111j1523-1739200500276x
Yukon Environmental and Socio-economic Assessment Board (YESAB) 2005Proponentrsquos guide to information requirements for executive committeeproject proposal submissions Available from httpsyesabcawpwp-contentuploads201304Proponents-Guide-to-Info-Requirements-for-EC-Project-Submissionpdf [last accessed 13 November 2018]
Yukon Environmental and Socio-economic Assessment Board (YESAB) 2019Consideration of Cumulative Effects in YESAB Assessments July 2019 ReportAvailable from httpsyesabca [last accessed 20 August 2020]
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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(Environmental Impact Statement for the Springbank Off-streamReservoir Project 2018) to estimate area changes in the amountof suitable habitat (based on habitat suitability models) for selectVCs Spraguersquos Pipit (Anthus spragueii) and Northern Leopard Frog(Lithobates pipiens) during different flood scenarios created by waterdiversion to a channel and reservoir within the proposed projectarea (Table 7)Effects-based CEAs typically involve the creation and application
of empirical speciesndashhabitat models within the study area Maceet al (1999) evaluated the CE of human and resource activity ongrizzly bear (Ursus arctos) habitat inwesternMontana during springsummer and fall seasons The authors modelled bear resourceselection from telemetry data satellite imagery elevation humanactivity points roads and trails and extrapolatedmodels to a largerregion with similar climate and vegetation Quantifying and map-ping areas with a high probability of use by bears in each seasonwas used to inform conservation and management actions (egroad or trail closures within important seasonal habitats habitatrestoration future road creation) Roumldder et al (2016) outline aninnovative approach to integrate SDM and potential connectivitymodels to identify connected and isolated populations and habitatcorridors for a species of conservation concern the Sand Lizard(Lacerta agilis) in western Germany (Fig 2) Quantifying potentialconnectivity areas (for movement like dispersal or migration) wasused to identify areas of strong connectivity persistent isolationand stable connective networks needed to connect populationsThis proposed framework may be particularly useful for proposeddevelopments that affect (i) portions of a previously larger inter-connected population or (ii) connective features between perma-nently colonized habitat patches Additional examples from thepublished literature are listed in Table 3
323 Advantages and limitations CEA questions and whenwhyto use3231 AdvantagesAdvantages of the habitat supply method include its ability to
quantify effects using loss of suitable habitat This approach
which has wide-spread application in wildlife research may besuitable when (i) data to map and measure habitat change areavailable but empirical data on species response to specific stres-sors are limited (ii) collaborative approaches focused on localissues and values are important (eg speciesndashhabitat associa-tions may be based on both qualitative (TK local and expertknowledge) and quantitative (speciesndashhabitat models) data) and(iii) data to develop empirical speciesndashhabitat models and adjusteddensity estimates are available so that total impacts can be com-pared to current and future population targets (eg Mahon et al2014)
3232 LimitationsLimitations to the habitat supply modelling approach include
the availability of accurate publicly available spatial data forhabitats and stressors and reliable specieshabitat associationsfrom the region of interest If additional data are required torefine existing habitat suitability models or create new empiricalspeciesndashhabitat models within a study area stakeholders or part-ners should a priori develop rigorous sampling designs and sur-vey protocols to meet project objectives We note two additionalcautions when using this approach Specieshabitat associationsbased on limited empirical data from within the region or datafrom outside the region require model validation and testing(Muir et al 2011) Finally stressor effects are limited to how stres-sors contribute to the loss of suitable habitat (habitat changesare used to infer population consequences) Habitat-based analy-sis alone may not be useful because (i) habitat is assumed to bea useful measure of population status (ii) habitat needs of the tar-get species or VC are assumed to be known (Schultz 2010) and(iii) specific stressor effects on the target species or VC are notquantified (eg the shape magnitude and direction of speciesrsquoresponse to stressors indirect or novel effects of stressors on speciesor interactions among stressors) Failure to identify which stressorsare influencing target species or VCsmay limit future research intobiological mechanisms (eg habitat selection movement disper-sal reproduction survival predatorndashprey dynamics) needed to
Fig 1 Example of a qualitative review (conceptual modeldiagram) for an effects-based CEA Figure represents an annual life cycle ofevents of Greater Sage-Grouse responding to oil and gas development in southwest Wyoming USA (from Heinrichs et al 2019 reproducedwith permission of Ecol Appl Vol 29 copy2019 Ecological Society of America)
Mahon and Pelech 211
Published by NRC Research Press
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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
218 Environ Rev Vol 29 2021
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
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Mahon and Pelech 221
Published by NRC Research Press
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iron
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Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
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Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
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Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
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Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
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Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
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Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
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Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
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Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
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Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
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Pearce J and Ferrier S 2000 Evaluating the predictive performance ofhabitat models developed using logistic regression Ecol Modell 133(3)225ndash245 doi101016S0304-3800(00)00322-7
Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
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Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
Sawyer H Kauffman MJ and Nielson RM 2009 Influence of well pad ac-tivity on winter habitat selection patterns of Mule Deer J Wildl Manage73(7) 1052ndash1061 doi1021932008-478
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Schultz C 2010 Challenges in connecting cumulative effects analysis toeffective wildlife conservation planning BioScience 60(7) 545ndash551 doi101525bio201060710
Shang Z He HS Xi W Shifley SR and Palik BJ 2012 IntegratingLANDIS model and a multi-criteria decision-making approach to evaluatecumulative effects of forest management in the Missouri Ozarks USAEcol Modell 229 50ndash63 doi101016jecolmodel201108014
Silva JP Santos M Queiroacutes L Leitatildeo D Moreira F Pinto M et al2010 Estimating the influence of overhead transmission power lies andlandscape context on the density of little bustard Tetrax tetrax breedingpopulations Ecol Modell 221 1954ndash1963 doi101016jecolmodel201003027
Mahon and Pelech 223
Published by NRC Research Press
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Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
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Stralberg D Wang X Parisien MA Robinne FN Soacutelymos P Mahon CLet al 2018 Wildfire-mediated vegetation change in boreal forests of AlbertaCanada Ecosphere 9 e02156 doi101002ecs22156
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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understand species responses and develop effective managementormitigation actions
3233 Science-based CEA questionsSee Table 4 to identify how habitat supply model methods
address key science-based CEA questions
3234 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species data are limited expert opinion
habitat suitabilitymodels can be used to quantify the loss of suitablehabitat When species data are available but data to model theresponse of species to key stressors are limited empirical speciesndashhabitat models can be used to quantify loss of suitable habitatModels can be extrapolated to larger regions to compare the cur-rent total cumulative loss of suitable habitat within proposedproject areas and larger regional areas (eg planning units orecologicalbiological regional units) Newly available environ-mental data (eg sentinel missions) could support speciesndashhabitatmodel development at finer spatial and temporal scales ldquoWhy touserdquo despite limitations and assumptions these flexible and diversemethods can allow for a comprehensive assessment of changes inthe amount of suitable habitat for multiple terrestrial speciesduring each stage of industrial or resource development (egbefore and after development after reclamation or restoration)within a proposed project and regional study area (eg project-based CEA) or larger regional study area (eg effects-based CEA)
33 Empirical species-stressormodels
331 Description of methodThis method combines carefully designed large-scale field stud-
ies and advanced statistical modelling to directly measure andquantify species responses to different stressor amounts and com-binations (rather than inferring effects based on changes in theamount of suitable habitat alone) Although some elements arecommon to empirical speciesndashhabitat models stressors and poten-tial stressor interactions are the primary factors of interest in designand analyses of empirical speciesndashstressor models Quantifying theindividual and combined effects of stressors on populations andidentifying their temporal and spatial scales of influence are keychallenges in assessing CE and planning future landscapes that sup-port populations of terrestrial species (Foley et al 2017 Heinrichset al 2019) Once stressor effects are identified additional work toidentify changes in ecological and behavioural processes (eg repro-duction survival habitat selection predator and parasite dynam-ics) can be used to develop and evaluate effective mitigation or
management actions The approach requires (i) targeted sam-pling of species metrics across a full gradient of stressor amountsstressor combinations and habitats and (ii) advanced design andinterpretation of statistical models to determine the shape magni-tude direction and additive or interactive effects ofmultiple stres-sors The approach is sometimes described as a dosendashresponseapproach although logistical and resource data constraints havelimited its application for many terrestrial species We provideadditional scientific background subsequently however for CEAthis approach generally involves three key steps
1 Assessing stressors and availability of adequately attributedstressor data
2 Developing amodel set and sampling design to ensure adequatesampling of key stressor combinations and gradients
3 Conducting statistical analyses and interpreting the often-complex results to provide land use and mitigation recom-mendations summarize data gaps and prioritize futureresearch and monitoring needs
Further we outline key design and analytical steps of theapproach focusing on technical considerations that are uniqueand (or) go beyond those typically used in empirical speciesndashhabitatmodels First in addition to spatial data commonly used for speciesndashhabitat models (eg landcover vegetation other environmentalvariables) the approach requires compiling and assessing theaccuracy of attributed stressor layers (eg including informa-tion on originating sector type of stressor year of origin andremoval reclamation status buffer width) Second GIS analysesare used to summarize and categorize stressor types amountsand degree of co-occurrence in the study area This informationcan be used to develop specific a priori hypotheses on how stres-sors combine (eg additively or interactively) to affect species inthe study area (often represented as model sets that are laterevaluated in statistical analyses see Table 8 as an example)Stressor and habitat layers are queried to create a targeted sam-pling design that ensures sufficient species data can be collectedacross the full range of stressor amounts and combinations(approximating a ldquofull factorialrdquo design to the extent possible)Given the large data requirements of speciesndashhabitatndashstressor
models existing species data from previous assessments researchor monitoring may be assessed for potential compatibility and inte-gration with data from targeted sampling designs (see Foster et al2017 for an example approach) The large resource investment of the
Table 6 Example of habitat supply models for a project-based CEA In this hypothetical 10 000 ha study area aproposed project is estimated to have a 500 ha development footprint most of which (400 ha) falls on the existing1000 ha of suitable habitat for a species
Note Project startoperation (ie application) and end (ie closure) conditions are compared to current (ie baseline) conditionsto estimate project-specific effects on species (suitable habitat is reduced from 10 to 6 of study area during the project but 0 afterremediation and closure) Cumulative effects are typically evaluated by summing the total area of existing stressors (35 of studyarea) and comparing to the area of the proposed project footprint (5) to indicate the projectrsquos relative or incremental effect
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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
218 Environ Rev Vol 29 2021
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
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Mahon and Pelech 221
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iron
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Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
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Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
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Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
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Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
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Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
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Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
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Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
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McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
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Smit B and Spaling H 1995 Methods for cumulative effects assessmentEnviron Impact Assess Rev 15 81ndash106 doi1010160195-9255(94)00027-X
Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
Steventon JD Sutherland GD and Arcese P 2006 A population-viability-based risk assessment of Marbled Murrelet nesting habitat policy in BritishColumbia Can J For Res 36(12) 3075ndash3086 doi101139x06-198
Stralberg D Wang X Parisien MA Robinne FN Soacutelymos P Mahon CLet al 2018 Wildfire-mediated vegetation change in boreal forests of AlbertaCanada Ecosphere 9 e02156 doi101002ecs22156
Sutherland GD Waterhouse FL Smith J Saunders SC Paige K andMalt J 2016 Developing a systematic simulation-based approach forselecting indicators in strategic cumulative effects assessments with mul-tiple environmental valued components Ecol Indic 61 512ndash525 doi101016jecolind201510004
Swor T and Canter L 2011 Promoting environmental sustainability via anexpert elicitation process Environ Impact Assess Rev 31 506ndash514 doi101016jeiar201101014
Tremblay JA Boulanger Y Cyr D Taylor AR Price DT and St-Laurent MH2018 Harvesting interacts with climate change to affect future habitat qualityof a focal species in eastern Canadarsquos boreal forest PLoS ONE 13 e0191645doi101371journalpone0191645 PMID29414989
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US Fish and Wildlife Service (USFWS) 1981 Standards for development ofhabitat suitability models 103 ESM US Department of the Interior Fishand Wildlife Service Washington DC
Usher PJ 2000 Traditional ecological knowledge in environmental assess-ment and management Arctic 53 183ndash193 doi1014430arctic849
Uusitalo L 2007 Advantages and challenges of Bayesian networks in environ-mental modelling Ecol Modell 203 312ndash318 doi101016jecolmodel200611033
Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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approach makes it particularly critical that new data collectionadhere to existing species sampling standardsFinally as with empirical speciesndashhabitat modelling a range of
statistical modeling frameworks are available to analyse speciesndashhabitatndashstressor relationships (Dersquoath and Fabricius 2000 Austin2007 Elith et al 2008 Elith and Leathwick 2009 Zuur et al 2009Hegel et al 2010) For example generalized linear models andassociated methods are commonly used in ecological researchand model coefficients can be readily interpreted and communi-cated For project-based or effects-based CEAs we recommend atwo-step modelling approach to (i) determine the best speciesndashhabitat model (a ldquobaselinerdquo model that includes habitat land-scape and sampling metrics already knownexpected to affect
species abundance) and (ii) test whether the addition of stressorvariables improves model fit and explains abundance patternsabove habitat loss alone Additional comparisons of paired addi-tive (A + B) and interactive (A + B + A B) models within themodel set can assess if simple additive or more complex interac-tive effects may be occurring (eg Mahon et al 2019) Interactiveeffects could be synergistic (the interaction of stressors increasesthe magnitude of the response above that seen for additiveeffects A + B + A B gt A + B) or antagonistic (the interaction ofstressors decreases the magnitude of the response below thatseen for additive effects A + B + A Blt A + B) As in speciesndashhabitatmodels the predictive ability of models can be evaluated usingexternal data although internal validation approaches (eg cross-
Table 7 Example of habitat supply models for a project-based CEA
Note The table represents changes in habitat area for multiple flood scenarios based on habitat suitability models for two valued components (Spraguersquos Pipitand Northern Leopard Frog) Source Environmental Impact Statement for the Springbank Off-stream Reservoir Project 2018
Fig 2 Example of habitat supply models for an effects-based CEA Figure represents a framework to quantify potential dispersal corridors forsensitive species by integrating a species distribution model with a connectivity model (from Roumldder et al 2016 reproduced with permission ofEnviron Manage Vol 58 copy2016 Springer) Notes mdash Resistance surface the species distribution map can be used as a resistance surface wherehigh suitability values indicate low resistance values after accounting for barriers (ie fragmentation thresholds) Fragmentation thresholdsbarriers that limit unrealistic movement paths in the landscape (eg disturbed area or large water body with low suitability values that cannotbe crossed by the species) Structural connectivity spatial arrangement of landscape elements
Mahon and Pelech 213
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nly
validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
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Mahon and Pelech 221
Published by NRC Research Press
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iron
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n 12
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Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
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Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
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222 Environ Rev Vol 29 2021
Published by NRC Research Press
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iron
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n 12
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Mahon and Pelech 223
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r pe
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Wintle BA Bekessy SA Venier LA Pearce JL and Chisholm RA 2005Utility of dynamic-landscape metapopulation models for sustainable forestmanagement Conserv Biol 19 1930ndash1943 doi101111j1523-1739200500276x
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Yukon Environmental and Socio-economic Assessment Board (YESAB) 2019Consideration of Cumulative Effects in YESAB Assessments July 2019 ReportAvailable from httpsyesabca [last accessed 20 August 2020]
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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validation methods) will likely be more common in this particu-larly data intense approach (Pearce and Ferrier 2000 Boyce et al2002) As in empirical habitat supply approaches final modelhabitat and stressor coefficients for each species can be com-bined with study area maps within GIS to evaluate species distri-bution and abundance given stressor conditions However it isstressor amounts and stressor combinations (additive or inter-active) not just habitat amounts that are used in these analy-ses Speciesndashhabitatndashstressor models can also be combined orintegrated with various landscape simulation models to predictfuture species abundance or density under alternate resourceand land management natural disturbance (wildfire floodsdrought insect outbreaks) and climate change scenarios
332 Examples of applicationsWe provide a number of examples to highlight the diversity
of analysis approaches within this broad category We focus onexamples that include multiple terrestrial species clearly definedobjectives and innovative and diverse sampling and analysis meth-ods Recent studies from northern Alberta Canada a region wheremultiple resource sectors (forestry agriculture conventional oiland gas exploration and development oilsands exploration anddevelopment) and intensive land use practices create co-occurringstressors across time and space have used versions of empiricalspeciesndashstressor modelling to assess CE on wildlife including bo-real birds (Schieck et al 2014 Soacutelymos et al 2015) and mammals(Fisher and Burton 2018) We highlight the latter study as aninnovative assessment using digital cameras to assess effects ofpolygonal (harvest units well sites industrial sites mines) andlinear stressors (seismic lines roads trails) on 10 terrestrialmammal species gray wolf white-tailed deer (Odocoileus virginianus)moose (Alces alces) American black bear coyote (Canis latrans)Canada lynx (Lynx canadensis) fisher (Pekania pennanti) red fox(Vulpes vulpes) snowshoe hare (Lepus americanus) and Americanred squirrel (Tamiasciurus hudsonicus) The authors usemodel coef-ficients to quantify the magnitude direction and statistical cer-tainty of individual stressor effects on mammal species relativeabundance to demonstrate the variability among species andstressors in an effective easy to interpret format (Fig 3)
Despite growing evidence of interactive effects as key factorsstructuring terrestrial and marine communities (Brown et al2013 Cartwright et al 2014) few studies have attempted to exam-ine these complex relationships in terrestrial systems Mahonet al (2019) examined additive and interactive effects of multiplepolygonal and linear stressors on boreal landbirds in northernAlberta The authors evaluated whether the effects of multiplestressors combined additively or interactively by testing a candi-date model set of 12 paired CEmodels of abundance for 27 speciesof boreal landbirds (Table 8) Creating speciesndashhabitat and speciesndashhabitatndashstressor models revealed that the addition of disturbanceeffects significantly improved models for 20 of the 27 (74) land-bird species Complex synergistic and antagonistic interactionsamong stressors were observed for 40 of landbird species withthe majority of interactions being synergistic suggesting thatboth direct effects (eg loss of native vegetation) and indirecteffects (eg altered biological interactions changes to behav-iour or vital rates) are affecting landbird populations within theregional study areaA number of studies have used a zone of influence (ZOI) approach
to assess how distance from stressors can influence the magnitudeof the stressor response for multiple terrestrial species (egClaireau et al 2019 Daniel and Koper 2019) These studies canbe useful to directly informmitigation or management actionsCareful study designs should attempt to (i) examine speciesresponses to all primary stressors (ii) include distance to stressoras a continuous variable (iii) avoid pre-defined zones (iv) use multi-ple response metrics (eg abundance movement or demographicvariables) and (v) assess underlying biological processes (eg habi-tat selection predatorndashprey interactions) to avoid potentiallymisleading results and oversimplified or misguided manage-ment actions Daniel and Koper (2019) estimated effects of dis-tance from energy-related infrastructure (oil wells shallow gaswells roads) and shallow gas well density on habitat use (abun-dance used as an index of habitat use) and productivity (nestsuccess clutch size) of five grassland songbird species theChestnut-collared Longspur (Calcarius ornatus) Spraguersquos PipitSavannah Sparrow (Passerculus sanwichensis) Vesper Sparrow(Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta)The authors assessed CE of all energy infrastructure on habitat use
Table 8 Example of empirical speciesndashstressor models for an effects-based CEA
Model group and set Model type Model predictors
Habitat only (HO)1a NA Habitat and spatial variables only
Linear (LI)2a Additive NL +WL2b Interactive NL +WL + NLWL
Linear and energy (LE)3a Additive NL +WL +WE3b Interactive NL +WL +WE +WENL +WEWL3c Interactive NL +WL +WE +WENL +WEWL +WENLWL
Linear and forestry (LF)4a Additive AL + HU4b Interactive AL + HU + HUAL
Cumulative (CU)5a Additive AL +WE + HU5b Interactive AL +WE + HU + HUAL + HUWE5c Interactive AL +WE + HU + HUAL + HUWE +WE AL + HUWE AL
Disturbance area (DA)6a Additive All disturbances summed (combined AL WE HU)
Note All models include habitat and spatial predictors selected for each landbird species Stressor definitions NLnarrow linear (percent cover of seismic lines) WL wide linear (percent cover of pipelines powerlines all roads) WE wells(percent cover of bitumen oil and natural gas wells) AL all linear (percent cover of narrow and wide linear combined) HUharvest unit (percent cover of harvest units with harvest activity lt20 years) Candidate model set represents an approach totest additive and interactive effects of multiple sectors and stressors on boreal landbirds within the Athabasca Oil SandsArea of Alberta Canada Source Mahon et al 2019
214 Environ Rev Vol 29 2021
Published by NRC Research Press
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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
vegetation layer including ldquobackfilledrdquo vegetation in human footprints(Version 6) Alberta Biodiversity Monitoring Institute Edmonton Reportavailable from httpsabmicahomehtml [last accessed 13 November 2018]
Austin M 2007 Species distribution models and ecological theory a criticalassessment and some possible new approaches Ecol Modell 200 1ndash19 doi101016jecolmodel200607005
Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
BC Resource Inventory Committee 1999 British Columbia Wildlife HabitatRating Standards Available from httpswww2govbccaassetsgovenvironmentnatural-resource-stewardshipnr-laws-policyriscwhrspdf [last accessed13 November 2018]
Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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r pe
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se o
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Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
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Canadian Environmental Assessment Agency (CEAA) Expert Panel Review 2017Building common ground a new vision for impact assessment in Canada Thefinal report of the expert panel for the review of Environmental assessmentprocesses Available from httpswwwcanadacaenservicesenvironmentconservationassessmentsenvironmental-reviewsenvironmental-assessment-processesbuilding-common-groundhtml [last accessed 10 April 2020]
Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
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Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
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Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
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Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
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Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
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Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
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Mahon and Pelech 223
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Smit B and Spaling H 1995 Methods for cumulative effects assessmentEnviron Impact Assess Rev 15 81ndash106 doi1010160195-9255(94)00027-X
Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
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Swor T and Canter L 2011 Promoting environmental sustainability via anexpert elicitation process Environ Impact Assess Rev 31 506ndash514 doi101016jeiar201101014
Tremblay JA Boulanger Y Cyr D Taylor AR Price DT and St-Laurent MH2018 Harvesting interacts with climate change to affect future habitat qualityof a focal species in eastern Canadarsquos boreal forest PLoS ONE 13 e0191645doi101371journalpone0191645 PMID29414989
US Fish and Wildlife Service (USFWS) 1980 Habitat as a basis for environ-mental assessment 101 ESM US Department of the Interior Fish andWildlife Service Washington DC
US Fish and Wildlife Service (USFWS) 1981 Standards for development ofhabitat suitability models 103 ESM US Department of the Interior Fishand Wildlife Service Washington DC
Usher PJ 2000 Traditional ecological knowledge in environmental assess-ment and management Arctic 53 183ndash193 doi1014430arctic849
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Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
Westwood AR Olszynski M Fox CH Ford AT Jacob AL Moore JWand Palen WJ 2019 The role of science in contemporary Canadian envi-ronmental decision making the example of environmental assessmentUniv Br Columbia Law Rev 52(1) 243ndash291
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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and productivity by comparing two types of impact models singlestrongest effect (eg strongest impact) and average effect (eg addi-tive impact) models As a final predictive map product for each birdspecies they used their model results (including threshold distan-ces) to estimate the species-specific cumulative areawithin the studyarea that was likely to have poor low medium or high habitatpotential for abundances clutch sizes or nesting successFinally a number of studies have used population models to
integrate life history (eg demographic parameters) stressoreffects and environmental effects (Salice et al 2011 Salice 2012Heinrichs et al 2019) to better understand how stressors actingon different life stages will affect population-level rates ofchange Hodgson et al (2017) and Katzner et al (2020) provideuseful frameworks to assess population level consequences ofstressors on terrestrial and aquatic species The five-step frame-work (Fig 4) proposed by Katzner et al (2020) may be particularlyuseful to upscale from individual-level effects to population-levelconsequences (ie from count-based data to rate-based estimatesdescribing the larger population) This type of framework is criti-cal to (i) interpreting the relevance of models describing the con-sequence of stressors on wildlife populations (eg assessing thesignificance to the population of the rate changes brought on bythe stressor or stressors) and (ii) developing effective conserva-tion or management actions (eg threshold for amount ofanthropogenic take) The authors provide relevant examples fortwo terrestrial birds Greater Roadrunner (Geococcyx californianus)affected by solar energy in the Mojave Desert and Red-tailed
Hawks (Buteo jamaicensis) affected by wind turbines in central Cal-ifornia Additional examples from the published literature arelisted in Table 3
333 Advantages and limitations CEA questions and whenwhyto use
3331 AdvantagesAdvantages to empirical speciesndashstressor modelling are that
species responses to stressors are measured directly (not inferredbased on changes to suitable habitat) Measuring and analyzingspecies responses across a full combination and gradient of stres-sors is the only way to achieve many of the key science-basedobjectives of CEA and quantitatively evaluate (i) individual stres-sor effects and an associated measure of certainty of the effect(ii) additive and interactive effects and (iii) shape magnitudeand direction of speciesstressor relationships This approachcan be used to identify the presence of complex indirect effectsbeyond habitat loss alone and focus new research on ecologicaland behavioural processes such as shifts to alternate food orprey and changes in reproduction or survival habitat selectionand predator or parasite dynamics Recent work on birds andmammals from multi-stressor landscapes in the western borealforest demonstrates the diversity and complexity of speciesresponses to individual and multiple stressors (see Soacutelymos et al2015 Fisher and Burton 2018 Mahon et al 2019) and identifiesthe need for process and whole system studies to develop biologi-cally relevant and effective mitigation or management actions
Fig 3 Example of empirical speciesndashstressor models for an effects-based CEA Figure represents estimated parameter coefficients (effectsizes) for the relationship between species abundance and landscape features (natural disturbed) from species distribution models(SDM) Some species relationships were positive (blue) and others were negative (red) for both natural features (triangles) and disturbancefeatures (circles) (from Fisher and Burton 2018 reproduced with permission of Front Ecol Environ Vol 16 copy2018 Ecological Society ofAmerica) Notes mdash Mean effect sizes of disturbance stressors on species distribution were equal or greater than those of natural landcover variables The direction and magnitude of these effects varied across species and stressor types (four species showed strong positiveassociations with industrial block features and three species showed strong negative associations with 3D seismic lines) Stressor definitionscutblock forest harvest block 3D seismic intensively clustered crossed three-dimensional seismic lines linear seismic long linear petroleumexploration lines pipeline oil or gas pipeline well site small square associated with oil or gas exploration road hard surface road blockfeature polygonal industrial sites trails soft surface trail
Mahon and Pelech 215
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Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
216 Environ Rev Vol 29 2021
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
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les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
218 Environ Rev Vol 29 2021
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
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tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
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Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
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Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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iron
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IFIC
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LA
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n 12
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r pe
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se o
nly
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Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
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Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
Environmental Impact Statement for the Springbank Off-stream ReservoirProject 2018 Environmental Impact Assessment of the Springbank Off-stream Reservoir Project Stantec Incorporated Available from httpsiaac-aeicgcca [last accessed 15 June 2020]
Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
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Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
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r pe
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
Impact Assessment Agency of Canada (IAAC) 2019 The Impact AssessmentProcess Timelines and Outputs Available from httpswwwcanadacaenimpact-assessment-agencyhtml [last accessed 20 August 2020]
Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
Johnson C 2016 Understanding the spatial responses of caribou to human-caused disturbance In Development of modeling tools to address cumulativeeffects on the summer range of the Bathurst caribou herd ndash a demonstrationproject Edited by JS Nishi and A Gunn Environment and Natural ResourcesGovernment of the Northwest Territories Yellowknife NT pp 13ndash36
Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
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Morrison ML Marcot BG and Mannan RW 2006 Wildlife-habitat rela-tionships concepts and applications 3rd ed Island Press WashingtonDC USA
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
Noble B Liu J and Hackett P 2017 The contribution of project environmentalassessment to assessing and managing cumulative effects individually andcollectively insignificant Environ Manage 59 531ndash545 doi101007s00267-016-0799-7 PMID27885387
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Pearce J and Ferrier S 2000 Evaluating the predictive performance ofhabitat models developed using logistic regression Ecol Modell 133(3)225ndash245 doi101016S0304-3800(00)00322-7
Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
Salice CJ 2012 Multiple stressors and amphibians Contributions of adversehealth effects and altered hydroperiod to population decline and extinctionJ Herpetol 46(4) 675ndash681 doi10167011-091
Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
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Mahon and Pelech 223
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Smit B and Spaling H 1995 Methods for cumulative effects assessmentEnviron Impact Assess Rev 15 81ndash106 doi1010160195-9255(94)00027-X
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Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
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Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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 (Utilizzare queste impostazioni per creare documenti Adobe PDF adatti per visualizzare e stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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 ltFEFF4f7f75288fd94e9b8bbe5b9a521b5efa7684002000410064006f006200650020005000440046002065876863900275284e8e55464e1a65876863768467e5770b548c62535370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c676562535f00521b5efa768400200050004400460020658768633002gtSUO 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 ltFEFF4f7f752890194e9b8a2d7f6e5efa7acb7684002000410064006f006200650020005000440046002065874ef69069752865bc666e901a554652d965874ef6768467e5770b548c52175370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c4f86958b555f5df25efa7acb76840020005000440046002065874ef63002gtgtgtCropMonoImages trueDefaultRenderingIntent RelativeColorimetericPreserveHalftoneInfo falseColorImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtCropGrayImages truePDFXOutputCondition ()SubsetFonts trueEncodeMonoImages trueCropColorImages truePDFXNoTrimBoxError truegtgtsetdistillerparamsltltPageSize [61207920]HWResolution [600600]gtgtsetpagedevice
Finally sampling for these large-scale studies is typically designedto capture a large number of species which allows for a proactiveand robust assessment of stressor effects on both individual speciesand the community (seeMahon et al 2019 for an example) For spe-cies at risk or species of high conservation concern data intensiveintegrated population models can be created to better understandwhen (eg life stage) and how (eg reproduction dispersal sur-vival) individual and multiple stressors influence populations (seeHeinrichs et al 2019 for an example and Katzner et al 2020 for aframework)
3332 LimitationsLimitations of this approach include intensive field projects
(time resources) to collect data across the full gradient of stres-sors stressor combinations and habitats technical expertise to
conduct modelling and availability of accurate and sufficientlyattributed spatial data In addition two modelling restrictionsalso limit this approach (i) stressors and stressor combinations(additive interactive) can only be modelled if they co-occurwithin the study area along a gradient from low to high intensityand can be sufficiently sampled and (ii) rare specialized andpatchily distributed species can only be modelled if sample sizerequirements for complex statistical models are met This mayinclude some but not all species of conservation or manage-ment concern (eg species listed ldquoat riskrdquo under the Species at RiskAct rare or specialist species) Including a large suite of speciescan be used to proactively assess species and community shiftsincluding the biotic homogenization of communities as a resultof human disturbance (Clavel et al 2011 Mahon et al 20162019)
Fig 4 Example of an empirical speciesndashstressor model framework (integrated population model) for an effects-based CEA Figure represents aframework to assess population-level consequences of human stressors on wildlife Square boxes are steps in the framework circles are outsideinformation required to process each step From Katzner et al (2020) reproduced (free of copyright) from Ecosphere Vol 11 copy2020 EcologicalSociety of America Notes mdash Approaches to understanding the demographic significance of human impacts include CIU counterfactual ofimpacted and unimpacted population (ratio-based comparison of scenarios derived from population models with and without the humanstressor) PBR potential biological removal (fixed harvest rate applied to a population while allowing the population to reach or maintain anoptimum) ABC acceptable biological change (estimate of the amount of human-associated take that results in a 33 probability of a definedpopulation target being achieved) DPD decline probability difference (probability that a population would decline in scenarios with andwithout a single human stressor)
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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
Mahon and Pelech 217
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Fig5
Exam
ple
ofaBay
esianbeliefnetwork
(BBN)mod
elforan
effects-based
CEAFigu
rerepresents
aco
nce
ptu
almodel
ofaBBN
ofim
pacts
ofhuman
recrea
tionon
brownbea
rsin
Alaska
USA
Bluesummaryparen
tnod
es(indep
enden
t)su
mmarizerecreation
alactivities
withsimilar
impactsB
eige
interm
ediate
childnod
es(dep
ende
nt)representmechan
ismsof
impact(displacemen
tfrom
high-a
ndlow-qualityhab
itaten
erge
ticcostsan
dnutritional
intake)G
reen
outputch
ildnod
esrepresentrespon
seor
dem
ographic
variab
les(cubsu
rvival
reproductiona
ndad
ultsu
rvival)From
Fortin
etal(20
16)reproduced(freeof
copyright)from
PLoS
One
Vol11copy20
16Pu
blic
Library
ofScience
(PLo
S)
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
ledge
(SK)or
data
Whitech
ildnod
es(dep
enden
t)representresp
onse
indices
andoutputnodesquestion
sof
interest
(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
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Mahon and Pelech 221
Published by NRC Research Press
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iron
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n 12
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r pe
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Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
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Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
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Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
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Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
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222 Environ Rev Vol 29 2021
Published by NRC Research Press
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iron
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n 12
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r pe
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se o
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Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
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224 Environ Rev Vol 29 2021
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nly
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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtDSCReportingLevel 0PDFXOutputConditionIdentifier ()PDFXCompliantPDFOnly falseAllowTransparency falsePreserveCopyPage trueUsePrologue falseStartPage 1MonoImageDownsampleThreshold 10GrayImageDownsampleThreshold 10CheckCompliance [None]CreateJDFFile falsePDFXSetBleedBoxToMediaBox trueEmbedOpenType falseOPM 0PreserveOverprintSettings falseUCRandBGInfo RemoveColorImageDownsampleThreshold 10MonoImageDict ltltK -1gtgtGrayImageDownsampleType AverageDescription ltltENU (Use these settings to create Adobe PDF documents suitable for reliable viewing and printing of business documents Created PDF documents can be opened with Acrobat and Adobe Reader 50 and later)PTB 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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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3333 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3334 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when species and stressor data are available or
can be collected empirical speciesndashhabitatndashstressor models canbe used to address key science-based CEA questions Models canbe used to assess community shifts or population level changesbetween historical or no-disturbance and current landscapes(Mahon et al 2019) Empirical speciesndashhabitatndashstressor modelscan also be applied to landscape simulation models to generaterealistic scenarios about future impacts of stressors climate andnatural disturbance regimes on species of interest When resour-ces are available (eg for high profile species at risk) we recom-mend integrated population modelling frameworks and approaches(see Katzner et al 2020 for a framework and examples) ldquoWhy to userdquodespite large data requirements these diverse methods canquantify direct (additive effects of loss of suitable habitat) and insome cases indirect effects (synergistic or antagonistic effectscaused by complex interactions) of multiple stressors on terres-trial species Additional demographic studies to identify howand when stressors affect specific life stages and vital rates (egreproductive output dispersal rate survival or mortality popu-lation growth rate probability of extinction) are key to developingintegrated populationmodels needed to (i) assess the significance tothe population of the rate changes brought on by the stressor orstressors (ii) develop biologically relevant thresholds for anthropo-genic or human-caused take and (iii) devise targeted and effectivemitigationmeasures
34 Decision support models
341 Description of methodFor this category we restrict our review of decision support
models to BBN models (also called belief networks probabilitynetworks Bayesian networks and Bayes nets) Use of BBNs in theecological sciences has expanded over the past two decades (Marcotet al 2001 Steventon et al 2006 Howes et al 2010MacCracken et al2013 Ban et al 2014 Fortin et al 2016 Mantyka-Pringle et al 2017)and descriptions and guidelines for their development and use havebeen published (Marcot et al 2006 McCann et al 2006 Uusitalo2007 Jensen and Nielsen 2007 Kragt 2009) In general BBN modelsallow users to graphically and quantitatively represent causendasheffectrelationships among ecosystem components (eg stressors hab-itats species) using a range of data types and sources (ie quali-tative and quantitative information) Technically BBN modelsconsist of three elements (i) nodes representing key predictoror explanatory variables and one or more response variables(ii) links between nodes that represent causendasheffect relation-ships and (iii) joint probabilities representing the belief thatnode states will have certain probabilities given the probabil-ities of the states of connected nodes (MacCracken et al 2013)Nodes represent only categorical variables continuous varia-bles must be divided into discrete categories or states Nodesthat do not have links to other nodes that influence their priorprobabilities are parent (independent) nodes Nodes that havelinks to other nodes that influence their prior probabilities arechild (dependent) nodes Child nodes with no outgoing linksare output nodes Each node contains a conditional probabilitytable that specifies the relationships among all combinationsof the states of the parent node and the states of the child nodewhere all probabilities sum to one Child nodes with more thanone link represent interactions among parent nodes and cap-ture the CE of multiple variables The value for each cell in aconditional probability table can be derived from empiricaldata or an expert elicitation process that requires experts to docu-ment their certainty about the relationships The final posterior
probabilities of values or states of the output nodes are calcu-lated in the network using standard Bayesian learning statistics(Spiegelhalter et al 1993) The algorithms in commercial BBNmodelling platforms (eg Hugin httpwwwhugincom Neticahttpwwwnorsyscom SamIam httpreasoningcsuclaedusamiamB-Course httpb-coursehiitfi see Uusitalo 2007) permit rapidupdating of probabilities throughout the network as data andevidence become availableA BBNmodelling approach canbe usedwithinCEA studies to effec-
tively support management decisions by (i) graphically presentingthe complexity of the ecosystem in a hierarchy that partitions theproblem into solvable steps (ii) accommodating qualitative opin-ions (eg costs benefits anduncertainties) froma variety of partnerscombining them with quantitative data and then incorporating allinformation into a formal evaluation of outcomes given specificactions and (iii) representing system variability and uncertaintyand their implications to management actions using probabil-ities (McCann et al 2006) BBN models provide flexible frame-works to assess multiple stressor effects using quantitative andqualitative information (including TK and SK information seeBeacutelisle et al 2018 for an example) Note that to meet many CEAobjectives (eg shape magnitude and direction of stressor effectsadditive or interactive stressor combinations) BBN models willneed to include quantitative data (eg cell values in conditionalprobability tables based on parameter estimates from empiricalspeciesndashstressor models) not qualitative data obtained from litera-ture reviews or qualitative reviewmethods
342 Examples of applicationsThe use of BBN in terrestrial and aquatic CEAs has been increas-
ing As the number of stressors within ecosystems increases sodoes the complexity of assessing andmeasuring additive or inter-active effects leading to incomplete information and the requireddata to support decisionmaking by resourcemanagersWeprovidea summary of two examples that use BBNmodels within a CEA forterrestrial species The first example uses a BBNmodel to assess theCE of recreational activities on brown bears (Ursus arctos) in Alaska(Fortin et al 2016) The authors combined information from a liter-ature review and a survey of brown bear experts into a BBN modelto assist managers when evaluating the potential impacts of humanrecreational activities (eg camping fishing trail use) on brownbears (Fig 5) Recreational activities were combined in summaryparent nodes based on whether the activities were regulated orunregulated and if they occurred in habitat containing concen-trated (eg salmon or salt-marsh meadows) or dispersed (eg cowparsnip) food resources for bears The mechanisms of impact (rep-resented by intermediate child nodes) included displacement fromhigh- and low-quality habitat energetic costs and nutritional intakewhile response variables (represented by output child nodes)included reproduction cub survival and adult survival Thecompleted model was used to compare potential consequenceson reproduction cub survival and adult survival of five manage-ment scenarios containing multiple recreation activities in brownbear habitat (see tables 3ndash5 in Fortin et al 2016) and identify man-agement actions (eg public education closures placement ofcampground trail and bear-viewing sites) The second exampleintegrates both TK and SK information within a BBN model toassess key questions about the CE of human stressors associatedwith multiple sectors (oil and gas oil sands forestry coal and ura-nium mining agriculture hydro-electric dams) on the ecosystemhealth of the Slave River Delta region in northern Canada using41 bird fish and water quality indicators and nine environmentalhealth indices (Mantyka-Pringle et al 2017 see Fig 6) The authorsrated overall ecosystem health as lower than in the past with SKindicators ranked as moderate and TK indicators ranked as poorand used their results to identify the need for (i) transboundarywater co-management agreements (ii) effective communication of
Mahon and Pelech 217
Published by NRC Research Press
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218 Environ Rev Vol 29 2021
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Fig6Exa
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ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
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(indep
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edbytrad
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cedwith
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17Elsevier)
Mahon and Pelech 219
Published by NRC Research Press
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
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Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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iron
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LA
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n 12
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r pe
rson
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se o
nly
Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
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Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
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Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
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Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
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iron
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n 12
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r pe
rson
al u
se o
nly
Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
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Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
Millspaugh JJ and Thompson FR III 2009 Models for planning wildlifeconservation in large landscapes Elsevier Amsterdam the Netherlands
Morrison ML Marcot BG and Mannan RW 2006 Wildlife-habitat rela-tionships concepts and applications 3rd ed Island Press WashingtonDC USA
Muir J Hawkes VC Tuttle KN and Mochizuk T 2011 Synthesis of habitatmodels used in the oil sands region LGL Report EA3259 Unpublished reportby LGL Limited Environmental Research Associates for the Cumulative Envi-ronmental Management Association (CEMA) mdash The Reclamation WorkingGroup (RWG) Fort McMurray Alta LGL Limited Sidney BC
Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
Noble B Liu J and Hackett P 2017 The contribution of project environmentalassessment to assessing and managing cumulative effects individually andcollectively insignificant Environ Manage 59 531ndash545 doi101007s00267-016-0799-7 PMID27885387
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Pearce J and Ferrier S 2000 Evaluating the predictive performance ofhabitat models developed using logistic regression Ecol Modell 133(3)225ndash245 doi101016S0304-3800(00)00322-7
Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
Salice CJ 2012 Multiple stressors and amphibians Contributions of adversehealth effects and altered hydroperiod to population decline and extinctionJ Herpetol 46(4) 675ndash681 doi10167011-091
Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
Sawyer H Kauffman MJ and Nielson RM 2009 Influence of well pad ac-tivity on winter habitat selection patterns of Mule Deer J Wildl Manage73(7) 1052ndash1061 doi1021932008-478
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Mahon and Pelech 223
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Smit B and Spaling H 1995 Methods for cumulative effects assessmentEnviron Impact Assess Rev 15 81ndash106 doi1010160195-9255(94)00027-X
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Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
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224 Environ Rev Vol 29 2021
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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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Fig5
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218 Environ Rev Vol 29 2021
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Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
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ystem
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Colouredparen
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(indep
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edbytrad
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17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
vegetation layer including ldquobackfilledrdquo vegetation in human footprints(Version 6) Alberta Biodiversity Monitoring Institute Edmonton Reportavailable from httpsabmicahomehtml [last accessed 13 November 2018]
Austin M 2007 Species distribution models and ecological theory a criticalassessment and some possible new approaches Ecol Modell 200 1ndash19 doi101016jecolmodel200607005
Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
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Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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iron
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Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
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Canadian Environmental Assessment Agency (CEAA) Expert Panel Review 2017Building common ground a new vision for impact assessment in Canada Thefinal report of the expert panel for the review of Environmental assessmentprocesses Available from httpswwwcanadacaenservicesenvironmentconservationassessmentsenvironmental-reviewsenvironmental-assessment-processesbuilding-common-groundhtml [last accessed 10 April 2020]
Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
Environmental Impact Statement for the Springbank Off-stream ReservoirProject 2018 Environmental Impact Assessment of the Springbank Off-stream Reservoir Project Stantec Incorporated Available from httpsiaac-aeicgcca [last accessed 15 June 2020]
Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
Impact Assessment Agency of Canada (IAAC) 2019 The Impact AssessmentProcess Timelines and Outputs Available from httpswwwcanadacaenimpact-assessment-agencyhtml [last accessed 20 August 2020]
Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
Johnson C 2016 Understanding the spatial responses of caribou to human-caused disturbance In Development of modeling tools to address cumulativeeffects on the summer range of the Bathurst caribou herd ndash a demonstrationproject Edited by JS Nishi and A Gunn Environment and Natural ResourcesGovernment of the Northwest Territories Yellowknife NT pp 13ndash36
Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
Noble B Liu J and Hackett P 2017 The contribution of project environmentalassessment to assessing and managing cumulative effects individually andcollectively insignificant Environ Manage 59 531ndash545 doi101007s00267-016-0799-7 PMID27885387
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Pearce J and Ferrier S 2000 Evaluating the predictive performance ofhabitat models developed using logistic regression Ecol Modell 133(3)225ndash245 doi101016S0304-3800(00)00322-7
Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
Salice CJ 2012 Multiple stressors and amphibians Contributions of adversehealth effects and altered hydroperiod to population decline and extinctionJ Herpetol 46(4) 675ndash681 doi10167011-091
Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
Sawyer H Kauffman MJ and Nielson RM 2009 Influence of well pad ac-tivity on winter habitat selection patterns of Mule Deer J Wildl Manage73(7) 1052ndash1061 doi1021932008-478
Schieck J Muhly T Huggard D Solymos P Pan D Heckbert S andBayne E 2014 Predicting the cumulative effects of human developmenton biodiversity in northeastern Alberta Petroleum Technology AllianceCanada (Alberta Upstream Petroleum Research Fund) Calgary AB Avail-able from httpsauprfptacorgecological-2a-tool-to-assess-cumulative-effects-of-development-on-biodiversity [last accessed 13 November 2018]
Schultz C 2010 Challenges in connecting cumulative effects analysis toeffective wildlife conservation planning BioScience 60(7) 545ndash551 doi101525bio201060710
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Silva JP Santos M Queiroacutes L Leitatildeo D Moreira F Pinto M et al2010 Estimating the influence of overhead transmission power lies andlandscape context on the density of little bustard Tetrax tetrax breedingpopulations Ecol Modell 221 1954ndash1963 doi101016jecolmodel201003027
Mahon and Pelech 223
Published by NRC Research Press
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Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
Steventon JD Sutherland GD and Arcese P 2006 A population-viability-based risk assessment of Marbled Murrelet nesting habitat policy in BritishColumbia Can J For Res 36(12) 3075ndash3086 doi101139x06-198
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Swor T and Canter L 2011 Promoting environmental sustainability via anexpert elicitation process Environ Impact Assess Rev 31 506ndash514 doi101016jeiar201101014
Tremblay JA Boulanger Y Cyr D Taylor AR Price DT and St-Laurent MH2018 Harvesting interacts with climate change to affect future habitat qualityof a focal species in eastern Canadarsquos boreal forest PLoS ONE 13 e0191645doi101371journalpone0191645 PMID29414989
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US Fish and Wildlife Service (USFWS) 1981 Standards for development ofhabitat suitability models 103 ESM US Department of the Interior Fishand Wildlife Service Washington DC
Usher PJ 2000 Traditional ecological knowledge in environmental assess-ment and management Arctic 53 183ndash193 doi1014430arctic849
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Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
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224 Environ Rev Vol 29 2021
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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtDSCReportingLevel 0PDFXOutputConditionIdentifier ()PDFXCompliantPDFOnly falseAllowTransparency falsePreserveCopyPage trueUsePrologue falseStartPage 1MonoImageDownsampleThreshold 10GrayImageDownsampleThreshold 10CheckCompliance [None]CreateJDFFile falsePDFXSetBleedBoxToMediaBox trueEmbedOpenType falseOPM 0PreserveOverprintSettings falseUCRandBGInfo RemoveColorImageDownsampleThreshold 10MonoImageDict ltltK -1gtgtGrayImageDownsampleType AverageDescription ltltENU (Use these settings to create Adobe PDF documents suitable for reliable viewing and printing of business documents Created PDF documents can be opened with Acrobat and Adobe Reader 50 and later)PTB 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stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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 ltFEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020be44c988b2c8c2a40020bb38c11cb97c0020c548c815c801c73cb85c0020bcf4ace00020c778c1c4d558b2940020b3700020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002egtNLD (Gebruik deze instellingen om Adobe PDF-documenten te maken waarmee zakelijke documenten betrouwbaar kunnen worden weergegeven en afgedrukt De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 50 en hoger)NOR 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 (Utilizzare queste impostazioni per creare documenti Adobe PDF adatti per visualizzare e stampare documenti aziendali in modo affidabile I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 50 e versioni successive)JPN 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 ltFEFF4f7f752890194e9b8a2d7f6e5efa7acb7684002000410064006f006200650020005000440046002065874ef69069752865bc666e901a554652d965874ef6768467e5770b548c52175370300260a853ef4ee54f7f75280020004100630072006f0062006100740020548c002000410064006f00620065002000520065006100640065007200200035002e003000204ee553ca66f49ad87248672c4f86958b555f5df25efa7acb76840020005000440046002065874ef63002gtgtgtCropMonoImages trueDefaultRenderingIntent RelativeColorimetericPreserveHalftoneInfo falseColorImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtCropGrayImages truePDFXOutputCondition ()SubsetFonts trueEncodeMonoImages trueCropColorImages truePDFXNoTrimBoxError truegtgtsetdistillerparamsltltPageSize [61207920]HWResolution [600600]gtgtsetpagedevice
Fig6Exa
mple
ofaqualitativereview
andBay
esianbeliefnetwork(BBN)modelF
igure
represents
aco
nceptual
mod
elof
aBBN
ofth
eSlav
eRiver
DeltaNorth
westTerritories
Can
adausingecosystem
hea
lthindicators
andindices
toad
dress
threequestions
(1)Is
thewater
safe
todrink
(2)Are
thefish
andwildlife
safe
toea
tan
d(3)Is
theecos
ystem
hea
lthy
Colouredparen
tnodes
(indep
enden
t)represent41
indicators
whereligh
tgrey
nodes
wereinform
edbytrad
itional
know
ledge
(TK)an
ddarkgrey
nodes
wereinform
edby
scientificknow
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(SK)or
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ildnod
es(dep
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(from
Man
tyka-Pringleet
al2
017reprodu
cedwith
permission
ofEnvironIntV
ol102
copy20
17Elsevier)
Mahon and Pelech 219
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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
vegetation layer including ldquobackfilledrdquo vegetation in human footprints(Version 6) Alberta Biodiversity Monitoring Institute Edmonton Reportavailable from httpsabmicahomehtml [last accessed 13 November 2018]
Austin M 2007 Species distribution models and ecological theory a criticalassessment and some possible new approaches Ecol Modell 200 1ndash19 doi101016jecolmodel200607005
Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
BC Resource Inventory Committee 1999 British Columbia Wildlife HabitatRating Standards Available from httpswww2govbccaassetsgovenvironmentnatural-resource-stewardshipnr-laws-policyriscwhrspdf [last accessed13 November 2018]
Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
Canadian Environmental Assessment Agency (CEAA) 2015 Assessing Cumula-tive Environmental Effects under the Canadian Environmental AssessmentAct 2012 March 2015 Report Available from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
Canadian Environmental Assessment Agency (CEAA) Expert Panel Review 2017Building common ground a new vision for impact assessment in Canada Thefinal report of the expert panel for the review of Environmental assessmentprocesses Available from httpswwwcanadacaenservicesenvironmentconservationassessmentsenvironmental-reviewsenvironmental-assessment-processesbuilding-common-groundhtml [last accessed 10 April 2020]
Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
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Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
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Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
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Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
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Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
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Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
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Mahon and Pelech 223
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Smit B and Spaling H 1995 Methods for cumulative effects assessmentEnviron Impact Assess Rev 15 81ndash106 doi1010160195-9255(94)00027-X
Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
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Swor T and Canter L 2011 Promoting environmental sustainability via anexpert elicitation process Environ Impact Assess Rev 31 506ndash514 doi101016jeiar201101014
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US Fish and Wildlife Service (USFWS) 1980 Habitat as a basis for environ-mental assessment 101 ESM US Department of the Interior Fish andWildlife Service Washington DC
US Fish and Wildlife Service (USFWS) 1981 Standards for development ofhabitat suitability models 103 ESM US Department of the Interior Fishand Wildlife Service Washington DC
Usher PJ 2000 Traditional ecological knowledge in environmental assess-ment and management Arctic 53 183ndash193 doi1014430arctic849
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Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
Westwood AR Olszynski M Fox CH Ford AT Jacob AL Moore JWand Palen WJ 2019 The role of science in contemporary Canadian envi-ronmental decision making the example of environmental assessmentUniv Br Columbia Law Rev 52(1) 243ndash291
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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downstream effects that blends and synthesizes TK and SK and(iii) global policy that supports indigenous peoplersquos culturesand rights Additional examples from the published literatureare listed in Table 3
343 Advantages and limitations CEA questions and whenwhyto use
3431 AdvantagesA central advantage of BBN models is the ability to combine
quantitative and qualitative data and associated data uncertain-ties when describing how stressors act to affect species or ecosys-tem components This feature is valuable when empirical dataare limited andwhen data information and knowledge from dif-ferent disciplines and partner groups are available Outcomesfrom multiple CEA projects using qualitative review (stressorrankings) habitat supply models (habitat rankings or suitabilitymeasures) or empirical speciesndashstressor models (direction andsize of effects from model coefficients additive or interactivestressor combinations) can be incorporated into a BBN structureto address CEA questions New data and information can quicklyand easily be used to update or refine the model and conditionalprobability tables and formally validate the BBN predictions (egcomparing predictions from the BBN to new observations andcalculating the overall accuracy) Sensitivity analysis can be usedto determine how input variables influence outcomes and sce-nario analysis can be used to assess how alternate conditions anddecisions influence outcomes The use of Bayesian statistics tocalculate probabilities and account for prior knowledge andmissing data means that this approach is suitable for small datasets or data sets with missing data which are typical in complexterrestrial systems with multiple species and stressors Finallythe graphical format similar to a conceptual model or influencediagram depicts relationships among predictor (stressor) andresponse (outcome) variables resulting in an improved under-standing of complex relationships and the analytical process orapproach
3432 LimitationsLimitations of BBN methods include the following challenges
(i) technical expertise to develop the unique framework of a BBNand the time and resources to compile datainformation to spec-ify relationships and (ii) empirical data to specify the probabilityvalues associated with stressor effects within the conditionalprobability tables If new data are required to populate condi-tional probability tables partners should a priori develop rigor-ous sampling designs and survey protocols for terrestrial speciestomeet proposed project objectives
3433 Science-based CEA questionsSee Table 4 to identify how empirical speciesndashstressor model
methods address key science-based CEA questions
3434 Whenwhy to use ndash project-based and effects-based CEAsldquoWhen to userdquo when data sources are a mixture of qualitative
and quantitative sources and systems require complex assess-ments (eg multiple resource sectors and overlapping stressorsland use practices and climate stressors) BBN models can pro-vide a structured and flexible framework to identify and estimateeffects of multiple stressors and investigate outcomes of specificmanagement scenarios (see Steventon et al 2006 and Fortin et al2016) ldquoWhy to userdquo despite data requirements (qualitative andquantitative data) and the technical expertise required to createthe BBNmodel this approach provides the opportunity for effec-tive co-production of knowledge using TK and SK where eachknowledge system is incorporated into the BBN (see Beacutelisle et al2018)
4 Recommendations and conclusionsWe hope our review will help partners and decision makers
understand the extent to which current analytical approaches(qualitative review habitat supply models empirical speciesndashstressor models and decision support models) can answer keyscience-based CEA questions Although CEA has a long historyscientific review and guidance on analytical methods and out-comes has been surprisingly lacking particularly for terrestrialspecies Of the methods reviewed previously empirical speciesndashstressor models and decision support models that incorporateoutcomes from speciesndashstressor models are the only analyticalmethods that provide answers to most science-based CEA ques-tions including understanding how different stressors combineto affect an individual species identifying the significance of sin-gle and multiple stressor effects on populations and quantifyingthe certainty of single and multiple stressor effects These meth-ods are consistent with a move toward larger more complexregional CEA within the peer-reviewed literature (Duinker andGreig 2006 Johnson et al 2011 Noble et al 2017) and the reports(CEAA Expert Panel Review 2017 of Environmental AssessmentProcesses) guidance (httpswwwcanadacaenimpact-assessment-agencyhtml) and acts (eg Impact Assessment Act 2019) issued by theGovernment of CanadaOur review also highlights that although qualitative review
and habitat supply approaches have been used extensively forproject-based CEAs inferences based on habitat change alonewill not be sufficient to quantify and understand novel and com-plex multiple stressor effects that will be increasingly importantto effective project-based or effects-based CEAs We recognizethat the higher data time financial and technical requirementsof empirical speciesndashstressor models and BBN models may placethese approaches beyond the scope of most project-based CEAsRegional collaborations among partners (federal and provincialterritorial government agencies conservation agencies Indige-nous communities and groups academia industry) to leveragedata expertise and resources will be necessary to conduct rigor-ous effects-based CEA for terrestrial species at larger regionalscales that address key science-based CEA questions Subsequentlywe provide suggestions and recommendations for moving forwardgiven these constraints First we briefly touch on two analysis-related elements of CEA that deserve mention but they werebeyond the scope of this reviewFirst our review was limited to analytical approaches based on
western science and data (eg SK) and did not explicitly addressinclusion of TK Indigenous communities and groups may beuniquely and disproportionately affected by proposed develop-ments and also possess long-term knowledge on species distri-butions population changes migration timing and potentialstressor effects that may be lacking or differ from conven-tional scientific data (eg Berkes et al 2000 Parlee et al 2012)How to best engage communities and include TK should bedecided by Indigenous communities and groups However whereof interest to stakeholders we note that each of the approaches wereview here offer options for co-production of TK and SK withinthe same analytical framework Approaches already based onqualitative data (eg expert-based speciesndashhabitat models fullyqualitative reviews) may pose fewer technical challenges toincluding TK For example Polfus et al (2014) and Bridger et al(2017) used TK and local knowledge to develop speciesndashhabitatmodels for caribou and furbearers respectively Recent workhas also demonstrated how TK may be blended with conven-tional quantitative modelling For example Johnson (2016) com-bined Elder knowledge of caribou harvest locations and rivercrossings with satellite collar point locations to quantitativelymodel caribou responses using habitat and disturbance varia-bles Mantyka-Pringle et al (2017) used BBNs to blend TK expertopinion and scientific data to assess a suite of ecosystem health
220 Environ Rev Vol 29 2021
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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
vegetation layer including ldquobackfilledrdquo vegetation in human footprints(Version 6) Alberta Biodiversity Monitoring Institute Edmonton Reportavailable from httpsabmicahomehtml [last accessed 13 November 2018]
Austin M 2007 Species distribution models and ecological theory a criticalassessment and some possible new approaches Ecol Modell 200 1ndash19 doi101016jecolmodel200607005
Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
BC Resource Inventory Committee 1999 British Columbia Wildlife HabitatRating Standards Available from httpswww2govbccaassetsgovenvironmentnatural-resource-stewardshipnr-laws-policyriscwhrspdf [last accessed13 November 2018]
Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
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Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
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Canadian Environmental Assessment Agency (CEAA) Expert Panel Review 2017Building common ground a new vision for impact assessment in Canada Thefinal report of the expert panel for the review of Environmental assessmentprocesses Available from httpswwwcanadacaenservicesenvironmentconservationassessmentsenvironmental-reviewsenvironmental-assessment-processesbuilding-common-groundhtml [last accessed 10 April 2020]
Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
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Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
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Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
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Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
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Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
Noble B Liu J and Hackett P 2017 The contribution of project environmentalassessment to assessing and managing cumulative effects individually andcollectively insignificant Environ Manage 59 531ndash545 doi101007s00267-016-0799-7 PMID27885387
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Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
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Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
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Mahon and Pelech 223
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Smit B and Spaling H 1995 Methods for cumulative effects assessmentEnviron Impact Assess Rev 15 81ndash106 doi1010160195-9255(94)00027-X
Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
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Tremblay JA Boulanger Y Cyr D Taylor AR Price DT and St-Laurent MH2018 Harvesting interacts with climate change to affect future habitat qualityof a focal species in eastern Canadarsquos boreal forest PLoS ONE 13 e0191645doi101371journalpone0191645 PMID29414989
US Fish and Wildlife Service (USFWS) 1980 Habitat as a basis for environ-mental assessment 101 ESM US Department of the Interior Fish andWildlife Service Washington DC
US Fish and Wildlife Service (USFWS) 1981 Standards for development ofhabitat suitability models 103 ESM US Department of the Interior Fishand Wildlife Service Washington DC
Usher PJ 2000 Traditional ecological knowledge in environmental assess-ment and management Arctic 53 183ndash193 doi1014430arctic849
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Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
Westwood AR Olszynski M Fox CH Ford AT Jacob AL Moore JWand Palen WJ 2019 The role of science in contemporary Canadian envi-ronmental decision making the example of environmental assessmentUniv Br Columbia Law Rev 52(1) 243ndash291
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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indictors in the Slave River Delta region of the Northwest Terri-tories Because many decision support frameworks like BBNsare specifically designed to combine quantitative and qualitativedata types and sources they are a frequent choice for studiesincorporating TK into ecological models (Beacutelisle et al 2018) andoffer great potential for collaborative CEASecond although we did not specifically review landscape sim-
ulation models such models have and should continue to be apowerful tool for regional CEA and regional land use planningAlthough simulation models require (i) technical expertise(ii) extensive data to parameterize specific model components(eg vegetation dynamics natural disturbance patterns eco-nomic development) and (iii) accurate speciesndashhabitat or speciesndashhabitatndashstressor models to include within the model or apply tomodel output they allow users to track how a range of stressors(resource and land use climate) natural disturbance regimes andclimate models under proposed or alternate development or man-agement scenarios could affect VCs or terrestrial species Severalmodelling platforms are available (eg ALCES httpwwwalcesca HexSim httpwwwhexsimnet InVEST httpwwwnaturalcapitalprojectorg LANDIS-II httpwwwlandis-iiorgNetLogo httpcclnorthwesternedunetlogo Repast Simphonyhttpsrepastgithubio SpaDES httpspadespredictiveecologyorg)Selection of a modelling platform will depend on the suitabilityof the platform to meet objectives (eg inclusion and quality ofkey economic process models to track changes in resource andland use stressors climate models to track changes in climateand vegetation models to track vegetation dynamics includingnatural succession and post-disturbance recovery) and the expertiseof project members Multiple examples exist in the literatureincluding population changes of terrestrial species under forestmanagement scenarios (Shang et al 2012 Mahon et al 2014 Lestonet al 2020) forest management industrial development andinfrastructure scenarios (Sutherland et al 2016) forest managementand climate scenarios (Wintle et al 2005 Bonnot et al 2013 2017Tremblay et al 2018 Cadieux et al 2020) and land use andinfrastructure scenarios (Silva et al 2010)Conducting effective CEA has many challenges (Jones 2016
Hodgson et al 2019) and appropriate use of analytical methods isno exception We suggest the following recommendations toimprove current analytical methods for both project-based andeffects-based CEAs for terrestrial species
1 Use standardized consistent and strategic implementationof CEA methods within smaller project-based CEAs ensureresults and data are available to better inform future assess-ments and view these as essential building blocks on which alarger regional CEA (eg effects-based CEA) can be conductedusing empirical speciesndashstressor models and (or) BBN modelsView CEAmethods and results as hierarchical building blockswithin and among CEA to improve efficiency quality engage-ment and transparency Use multiple methods within oneproject-based or effects-based CEA in a tiered or hierarchicalframework For example use qualitative review to create con-ceptual models and describe the system and (or) the analysisprocess Use specific habitat supply models (qualitative orquantitative) to assess changes in suitable habitat for a largenumber of terrestrial species Use empirical speciesndashstressormodels and integrated population models to assess stressoreffects for a smaller number of terrestrial species of high con-servation concern where additional data from existing stud-ies may be available Use flexible decision support models likeBBN models to incorporate qualitative and quantitative dataexamine interactions among stressors and evaluate manage-ment scenarios for a large suite of terrestrial species of stew-ardship cultural management and conservation concern
2 Prioritize the availability accuracy and consistency of data(spatial nonspatial) for project-based and effects-based CEAsincluding sector resource and land use stressor climate stres-sor landcover and habitat and species data
3 Support the continued use of decision support models (egBBN) for project-based and effects-based CEAs that can repre-sent complex ecosystems and species combine multiple datasources facilitate co-production of TK and SK and reportuncertainties
Our review addresses a key challenge to project-based andeffects-based CEAs inconsistency in methods to conduct CEA(Hodgson et al 2019) Providing a clear understanding of com-mon CEA methods science-based CEA questions and when andwhy to use methods will allow proponents or partners to selectappropriate designs and analyses before undertaking a project-based or effects-based CEA Our review also identifies methodssuitable for larger regional CEA projects including RegionalAssessments conducted for or by the IAAC We suggest that effec-tive regional CEA projects for terrestrial species will require acollaborative multi-partner approach Regional CEA projectsprovide the spatial scale necessary to capture the relevant rangeof stressor and stressor combinations encountered by terrestrialspecies and more importantly the pooling of data expertiseand resources necessary to conduct the analytical methodsneeded to answer key science-based CEA questions We there-fore encourage policy changes that support regional effects-based CEA and technical and analytical changes that supportthe production of rigorous consistent and transparent assess-ments of CE for project-based and effects-based CEAs in alljurisdictions in Canada The latter requires (i) the implementa-tion and integration of large-scale monitoring programs forterrestrial species (eg multi-taxa monitoring Alberta Biodi-versity Monitoring Institute boreal bird monitoring BorealOptimized Sampling Strategy (Van Wilgenburg et al 2020))(ii) the consolidation and free access to stressor landcoverhabi-tat and species data and all data from past and current project-based CEAs and (iii) the use of appropriate analytical methodsto address science-based CEA objectives for terrestrial species
AcknowledgementsSupport for this work was provided by the Northern Region of
the Canadian Wildlife Service within Environment and ClimateChange Canada We thank Nathalie Lowry for input ideas andreview of an early draft and Myra Robinson for her support of thisproject
ReferencesAlberta Biodiversity Monitoring Institute (ABMI) 2017 Alberta wall-to-wall
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Austin M 2007 Species distribution models and ecological theory a criticalassessment and some possible new approaches Ecol Modell 200 1ndash19 doi101016jecolmodel200607005
Ban SS Pressey RL and Graham NAJ 2014 Assessing interactions ofmultiple stressors when data are limited a Bayesian belief network appliedto coral reefs Global Environ Change 27 64ndash72 doi101016jgloenvcha201404018
BC Ministry of Forests Lands and Natural Resources Operations 2016 Cumu-lative effects framework interim policy Available from httpswww2govbccagovcontentenvironmentnatural-resource-stewardshipcumulative-effects-framework [last accessed 13 November 2018]
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Beacutelisle AC Asselin H LeBlanc P and Gauthier S 2018 Local knowledge in ec-ological modeling Ecol Soc 23(2) 14ndash23 doi105751ES-09949-230214
Mahon and Pelech 221
Published by NRC Research Press
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Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
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Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
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Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
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Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
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Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
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McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
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Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
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Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
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Mahon and Pelech 223
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Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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Berkes F Colding J and Folke C 2000 Rediscovery of traditional ecologicalknowledge as adaptive management Ecol Appl 10 1251ndash1262 doi1018901051-0761(2000)010[1251ROTEKA]20CO2
Bonnot TW Thompson FR III Millspaugh JJ and Jones-Farrand DT2013 Landscape-based population viability models demonstrate importanceof strategic conservation planning for birds Biol Conserv 165 104ndash114 doi101016jbiocon201305010
Bonnot TW Thompson FR III and Millspaugh JJ 2017 Dynamic-landscapemetapopulation models predict complex response of wildlife populations toclimate and landscape change Ecosphere 8 e01890 doi101002ecs21890
Boyce MS Vernier PR Nielsen SE and Schmiegelow FK 2002 Evaluatingresource selection functions Ecol Modell 157(2ndash3) 281ndash300 doi101016S0304-3800(02)00200-4
Bridger MC Johnson CJ and Gillingham MP 2017 Working with experts toquantify changes in the abundance of furbearers following rapid and large-scale forest harvesting For Ecol Manage 402 194ndash203 doi101016jforeco201707045
Brown CJ Saunders MI Possingham HP and Richardson AJ 2013Managing for interactions between local and global stressors of ecosys-tems PLoS ONE 8 e65765 doi101371journalpone0065765 PMID23776542
Burton AC Huggard D Bayne E Schieck J Soacutelymos P Muhly T et al2014 A framework for adaptive monitoring of the cumulative effects ofhuman footprint on biodiversity Environ Monit Assess 186 3605ndash3617doi101007s10661-014-3643-7 PMID24488328
Cadieux P Boulanger Y Cyr D Taylor AR Price DT Soacutelymos P et al2020 Projected effects of climate change on boreal bird community accentu-ated by anthropogenic disturbances in western boreal forest Canada DiversDistrib 26 668ndash682 doi101111ddi13057
Canadian Environmental Assessment Agency (CEAA) 2014 Technical Guidancefor Assessing Cumulative Environmental Effects under the Canadian Envi-ronmental Assessment Act 2012 December 2014 (Draft) Report Availablefrom httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
Canadian Environmental Assessment Agency (CEAA) 2015 Assessing Cumula-tive Environmental Effects under the Canadian Environmental AssessmentAct 2012 March 2015 Report Available from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidancehtml [last accessed 20 August 2020]
Canadian Environmental Assessment Agency (CEAA) Expert Panel Review 2017Building common ground a new vision for impact assessment in Canada Thefinal report of the expert panel for the review of Environmental assessmentprocesses Available from httpswwwcanadacaenservicesenvironmentconservationassessmentsenvironmental-reviewsenvironmental-assessment-processesbuilding-common-groundhtml [last accessed 10 April 2020]
Canadian Environmental Assessment Agency (CEAA) 2018 Technical guidancefor assessing cumulative environmental effects under the Canadian Environ-mental Assessment Act 2012 March 2018 (Interim Technical Guidance) ReportAvailable from httpswwwcanadacaenimpact-assessment-agencyservicespolicy-guidance [last accessed 20 August 2020]
Canter LW and Atkinson SF 2011 Multiple uses of indicators and indicesin cumulative effects assessment and management Environ ImpactAssess Rev 31 491ndash501 doi101016jeiar201101012
Cartwright SJ Nicoll MA Jones CG Tatayah V and Norris K 2014Agriculture modifies the seasonal decline of breeding success in a tropi-cal wild bird population J Appl Ecol 51 1387ndash1395 doi1011111365-266412310 PMID25558086
Chetkiewicz C and Lintner AM 2014 Getting it right in Ontariorsquos norththe need for regional strategic environmental assessment in the Ring of Fire[Wawangajing] Wildlife Conservation Society and Ecojustice Toronto Ont
Chilima JS Gunn JAE Noble BF and Patrick RJ 2013 Institutionalconsiderations in watershed cumulative effects assessment and manage-ment Impact Assess Project Appraisal 31(1) 74ndash84 doi101080146155172012760227
Claireau F Bas Y Pauwels J Barreacute K Machon N Allegrini B et al 2019Major roads have important negative effects on insectivorous bat activityBiol Conserv 235 53ndash62 doi101016jbiocon201904002
Clarke H 2012 Knowledge-based habitat suitability modeling guidelinesYukon Fish and Wildlife Branch Report TR-12-18 Whitehorse YT
Clavel J Julliard R and Devictor V 2011 Worldwide decline of specialistspecies toward a global functional homogenization Front Ecol Environ 9222ndash228 doi101890080216
Cocklin C Parker S and Hay J 1992 Notes on cumulative environmentalchange II A contribution to methodology J Environ Manage 35 51ndash67doi101016S0301-4797(05)80127-6
Cooper LM 2011 CEA in policies and plans UK case studies Environ ImpactAssess Rev 31 465ndash480 doi101016jeiar201101009
Crain CM Kroeker K and Halpern BS 2008 Interactive and cumulativeeffects of multiple human stressors in marine systems Ecol Lett 11 1304ndash1315 doi101111j1461-0248200801253x PMID19046359
Daniel J and Koper N 2019 Cumulative impacts of roads and energyinfrastructure on grassland songbirds Condor 121(2) duz011 doi101093condorduz011
Darling ES and Cocircteacute IM 2008 Quantifying the evidence for ecologicalsynergies Ecol Lett 11 1278ndash1286 doi101111j1461-0248200801243x PMID18785986
Dersquoath G and Fabricius KE 2000 Classification and regression trees apowerful yet simple technique for ecological data analysis Ecology 813178ndash3192 [1018900012-9658(2000)081[3178CARTAP]20CO2]
Didham RK Tylianakis JM Gemmell NJ Rand TA and Ewers RM2007 Interactive effects of habitat modification and species invasion onnative species decline Trends Ecol Evol 22 489ndash496 doi101016jtree200707001 PMID17673330
Dubeacute M and Munkittrick K 2001 Integration of effects-based and stressor-based approaches into a holistic framework for cumulative effects assess-ment in aquatic ecosystems Hum Ecol Risk Assess 7 247ndash258 doi10108020018091094367
Duinker PN and Greig LA 2006 The impotence of cumulative effectsassessment in Canada ailments and ideas for redeployment EnvironManage 37 153ndash161 doi101007s00267-004-0240-5 PMID16362488
Duinker PN Burbidge EL Boardley SR and Greig LA 2013 Scientificdimensions of cumulative effects assessment toward improvements inguidance for practices Environ Rev 21(1) 40ndash52 doi101139er-2012-0035
Elith J and Leathwick JR 2009 Species distribution models ecological ex-planation and prediction across space and time Annu Rev Ecol EvolSyst 40 677ndash697 doi101146annurevecolsys110308120159
Elith J Leathwick JR and Hastie T 2008 A working guide to boostedregression trees J Anim Ecol 77 802ndash813 doi101111j1365-2656200801390x
Environmental Impact Statement for the Marathon PGM-Cu Project 2012Assessment of Potential Effects of the Proposed Marathon PGM-Cu Pro-ject Stillwater Canada Incorporated Available from httpsiaac-aeicgcca[last accessed 15 June 2020]
Environmental Impact Statement for the Springbank Off-stream ReservoirProject 2018 Environmental Impact Assessment of the Springbank Off-stream Reservoir Project Stantec Incorporated Available from httpsiaac-aeicgcca [last accessed 15 June 2020]
Fisher JT and Burton AC 2018 Wildlife winners and losers in an oilsands landscape Front Ecol Environ 16(6) 323ndash328 doi101002fee1807
Foley MM Mease LA Martone RG Prahler EE Morrison THMurray CC and Wojcik D 2017 The challenges and opportunities incumulative effects assessment Environ Impact Assess Rev 62 122ndash134doi101016jeiar201606008
Fortin JK Rode KD Hilderbrand GV Wilder J Farley S Jorgensen C andMarcot BG 2016 Impacts of human recreation on brown bears (Ursus arctos)A review and new management tool PLoS ONE 11(1) e0141983 doi101371journalpone0141983 PMID26731652
Foster SD Hosack GR Lawrence E Przeslawski R Hedge P Caley MJet al 2017 Spatially balanced designs that incorporate legacy sites MethodsEcol Evol 8 1433ndash1442 doi1011112041-210X12782
Gillingham MP Halseth GR Johnson CJ and Parkes MW 2016 Theintegration imperative cumulative environmental community and healtheffects of multiple natural resource developments Springer InternationalPublishing New York
Goodale MW and Milman A 2019 Assessing the cumulative exposure ofwildlife to offshore wind energy development J Environ Manage 23577ndash83 doi101016jjenvman201901022 PMID30677658
Gray SA Gray S De Kok JL Helfgott AER OrsquoDwyer B Jordan Rand Nyaki A 2015 Using fuzzy cognitive mapping as a participatoryapproach to analyze change preferred states and perceived resilience ofsocial-ecological systems Ecol Soc 20(2) 11 doi105751ES-07396-200211
Guisan A and Thuiller W 2005 Predicting species distribution offeringmore than simple habitat models Ecol Lett 8 993ndash1009 doi101111j1461-0248200500792x
Guisan A and Zimmermann NE 2000 Predictive habitat distributionmodels in ecology Ecol Modell 135 147ndash186 doi101016S0304-3800(00)00354-9
Guisan A Tingley R Baumgartner JB Naujokaitis-Lewis I Sutcliffe PRTulloch AIT et al 2013 Predicting species distributions for conservationdecisions Ecol Lett 16 1424ndash1435 doi101111ele12189 PMID24134332
Gunn J and Noble BF 2011 Conceptual and methodological challenges tointegrating SEA and cumulative effects assessment Environ Impact AssessRev 31 154ndash160 doi101016jeiar200912003
Gunn A Russell D and Greig L 2014 Insights into integrating cumulativeeffects and collaborative co-management for migratory tundra caribou herdsin the Northwest Territories Canada Ecol Soc 19(4) 4 doi105751ES-06856-190404
Hegel TM Cushman SA Huettmann F and Evans J 2010 Current stateof the art for statistical modelling of species distributions In SpatialComplexity Informatics and Wildlife Conservation Edited by F Huettmannand SA Cushman Springer-Verlag Tokyo Japan pp 1ndash458
Heinrichs JA OrsquoDonnell MS Aldridge CL Garman SL and Homer CG2019 Influences of potential oil and gas development and future climate onSage-grouse declines and redistribution Ecol Appl 29(6) e01912 doi101002eap1912 PMID31310420
Hodgson EE and Halpern BS 2019 Investigating cumulative effects across eco-logical scales Conserv Biol 33 22ndash32 doi101111cobi13125 PMID29722069
Hodgson EE Essington TE and Halpern BS 2017 Density dependencegoverns when population responses to multiple stressors are magnifiedor mitigated Ecology 98(10) 2673ndash2683 doi101002ecy1961 PMID28734087
222 Environ Rev Vol 29 2021
Published by NRC Research Press
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Hodgson EE Halpern BS and Essington TE 2019 Moving beyond silosin cumulative effects assessment Front Ecol Evol 7 211 doi103389fevo201900211
Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
Impact Assessment Agency of Canada (IAAC) 2019 The Impact AssessmentProcess Timelines and Outputs Available from httpswwwcanadacaenimpact-assessment-agencyhtml [last accessed 20 August 2020]
Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
Johnson C 2016 Understanding the spatial responses of caribou to human-caused disturbance In Development of modeling tools to address cumulativeeffects on the summer range of the Bathurst caribou herd ndash a demonstrationproject Edited by JS Nishi and A Gunn Environment and Natural ResourcesGovernment of the Northwest Territories Yellowknife NT pp 13ndash36
Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
Mackenzie Valley Environmental Impact Review Board (MVEIRB) 2004Environmental Impact Assessment Guidelines March 2004 Report avail-able from httpsreviewboardca [last accessed 20 August 2020]
Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
Millspaugh JJ and Thompson FR III 2009 Models for planning wildlifeconservation in large landscapes Elsevier Amsterdam the Netherlands
Morrison ML Marcot BG and Mannan RW 2006 Wildlife-habitat rela-tionships concepts and applications 3rd ed Island Press WashingtonDC USA
Muir J Hawkes VC Tuttle KN and Mochizuk T 2011 Synthesis of habitatmodels used in the oil sands region LGL Report EA3259 Unpublished reportby LGL Limited Environmental Research Associates for the Cumulative Envi-ronmental Management Association (CEMA) mdash The Reclamation WorkingGroup (RWG) Fort McMurray Alta LGL Limited Sidney BC
Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
Noble B Liu J and Hackett P 2017 The contribution of project environmentalassessment to assessing and managing cumulative effects individually andcollectively insignificant Environ Manage 59 531ndash545 doi101007s00267-016-0799-7 PMID27885387
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Pearce J and Ferrier S 2000 Evaluating the predictive performance ofhabitat models developed using logistic regression Ecol Modell 133(3)225ndash245 doi101016S0304-3800(00)00322-7
Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
Salice CJ 2012 Multiple stressors and amphibians Contributions of adversehealth effects and altered hydroperiod to population decline and extinctionJ Herpetol 46(4) 675ndash681 doi10167011-091
Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
Sawyer H Kauffman MJ and Nielson RM 2009 Influence of well pad ac-tivity on winter habitat selection patterns of Mule Deer J Wildl Manage73(7) 1052ndash1061 doi1021932008-478
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Mahon and Pelech 223
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Smit B and Spaling H 1995 Methods for cumulative effects assessmentEnviron Impact Assess Rev 15 81ndash106 doi1010160195-9255(94)00027-X
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Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
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224 Environ Rev Vol 29 2021
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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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Houle H Fortin D Dussault C Courtois R and Ouellet J-P 2010 Cu-mulative effects of forestry on habitat use by gray wolf (Canis lupus) inthe boreal forest Landsc Ecol 25 419ndash433 doi101007s10980-009-9420-2
Howes AL Maron M and McAlpine CA 2010 Bayesian networks andadaptive management of wildlife habitat Conserv Biol 24 974ndash983doi101111j1523-1739201001451x PMID20184652
Huntington HP 1998 Observations on the utility of the semi-directiveinterview for documenting traditional ecological knowledge Arctic 51237ndash242 doi1014430arctic1065
Impact Assessment Agency of Canada (IAAC) 2019 The Impact AssessmentProcess Timelines and Outputs Available from httpswwwcanadacaenimpact-assessment-agencyhtml [last accessed 20 August 2020]
Jensen FV and Nielsen TD 2007 Bayesian networks and decision graphsSpringer New York USA
Johnson C 2016 Understanding the spatial responses of caribou to human-caused disturbance In Development of modeling tools to address cumulativeeffects on the summer range of the Bathurst caribou herd ndash a demonstrationproject Edited by JS Nishi and A Gunn Environment and Natural ResourcesGovernment of the Northwest Territories Yellowknife NT pp 13ndash36
Johnson CJ Boyce MS Case RL Cluff HD Gau RJ Gunn A andMulders R 2005 Cumulative effects of human development of arcticwildlife Wildl Monogr 160 1ndash36
Johnson D Lalonde K McEachern M Kenney J Mendoza G Buffin Aand Rich K 2011 Improving cumulative effects assessment in AlbertaRegional strategic assessment Environ Impact Assess Rev 31 481ndash483doi101016jeiar201101010
Jones FC 2016 Cumulative effects assessment theoretical underpinningsand big problems Environ Rev 24 187ndash204 doi101139er-2015-0073
Jones FC Plewes R Murison L MacDougall MJ Sinclair S Davies Cet al 2017 Random forests as cumulative effects models A case study oflakes and rivers in Muskoka Canada J Environ Manage 201 407ndash424doi101016jjenvman201706011 PMID28704731
Katzner TE Braham MA Conkling TJ Diffendorfer JE Duerr ADLoss SR et al 2020 Assessing population-level consequences of anthro-pogenic stressors for terrestrial wildlife Ecosphere 11(3) e03046doi101002ecs23046
Kragt ME 2009 A beginnerrsquos guide to Bayesian network modelling forintegrated catchment management Landscape Logic Technical Reportno 9 Australian Government Department of the Environment WaterHeritage and the Arts Canberra ACT Australia
Leston L Bayne E Dzus E Soacutelymos P Moore T Andison D et al2020 Quantifying long-term bird population responses to simulated har-vest plans and cumulative effects of disturbance Front Ecol Evol 8 252doi103389fevo202000252
Lindenmayer DB and Likens GE 2010 Effective Ecological MonitoringCSIRO Publishing Collingwood Australia
Linstone HA and Turoff M 2002 The Delphi method techniques andapplications Addison-Wesley Reading Massachusetts USA
MacCracken JG Garlich-Miller J Snyder J and Meehan R 2013 Bayes-ian belief network models for species assessments an example with thePacific Walrus Wildl Soc Bull 37(1) 226ndash235 doi101002wsb229
Mace RD Waller JS Manley TL Ake K and Wittinger WT 1999 Land-scape evaluation of Grizzly Bear habitat in Western Montana ConservBiol 13(2) 367ndash377 doi101046j1523-17391999013002367x
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Mahon CL Bayne EM Soacutelymos P Matsuoka SM Carlson M Dzus Eet al 2014 Does expected future landscape condition support proposedpopulation objectives for boreal birds For Ecol Manage 312 28ndash39doi101016jforeco201310025
Mahon CL Holloway G Soacutelymos P Cumming SG Bayne EMSchmiegelow FKA and Song SJ 2016 Community structure and nichecharacteristics of upland and lowland western boreal birds at multiple spa-tial scales For Ecol Manage 361 99ndash116 doi101016jforeco201511007
Mahon CL Holloway GL Bayne EM and Toms JD 2019 Additive andinteractive cumulative effects on boreal landbirds winners and losers ina multi-stressor landscape Ecol Appl 29(5) e01895 doi101002eap1895PMID31121076
Mantyka-Pringle CS Martin TG Moffatt DB Udy J Olley J Saxton Net al 2016 Prioritizing management actions for the conservation of fresh-water biodiversity under changing climate and land-cover Biol Conserv 19780ndash89 doi101016jbiocon201602033
Mantyka-Pringle CS Jardine TD Bradford L Bharadwaj L Kythreotis APFresque-Baxter J et al 2017 Bridging science and traditional knowledge toassess cumulative impacts of stressors on ecosystem health Environ Int 102125ndash137 doi101016jenvint201702008 PMID28249740
Marcot BG Holthausen RS Raphael MG Rowland MM and Wisdom MJ2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability under land management alternatives from an environmental impactstatement For Ecol Manage 153 29ndash42 doi101016S0378-1127(01)00452-2
Marcot BG Steventon JD Sutherland GD and McCann RK 2006Guidelines for developing and updating Bayesian belief networks appliedto ecological modeling and conservation Can J For Res 36(12) 3063ndash3074 doi101139x06-135
Matsuoka SM Mahon CL Handel CM Soacutelymos P Bayne EM Fontaine PCand Ralph CJ 2014 Reviving common standards in point-count surveys forbroad inference across studies Condor 116 599ndash608 doi101650CONDOR-14-1081
McCann RK Marcot BG and Ellis R 2006 Bayesian belief networksapplications in ecology and natural resource management Can J ForRes 36(12) 3053ndash3062 doi101139x06-238
McNay RS Marcot BG Brumovsky V and Ellis R 2006 A Bayesianapproach to evaluating habitat for woodland caribou in north-centralBritish Columbia Can J For Res 36(12) 3117ndash3133 doi101139x06-258
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Munns WR Jr 2006 Assessing risks to wildlife populations from multiplestressors Overview of the problem and research needs Ecol Soc 11(1) 23
Nelitz MA Beardmore B Machtans CS Hall AW and Wedeles C2015 Addressing complexity and uncertainty conceptual models and expertjudgments applied to migratory birds in the oil sands of Canada EcolSoc 20(4) 4 doi105751ES-07906-200404
Noble B 2010 Cumulative environmental effects and the tyranny of smalldecisions towards meaningful cumulative effects assessment and manage-ment Natural Resources and Environmental Studies Institute University ofNorthern British Columbia Occasional Paper No 8 Prince George BC
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Pearce J and Ferrier S 2000 Evaluating the predictive performance ofhabitat models developed using logistic regression Ecol Modell 133(3)225ndash245 doi101016S0304-3800(00)00322-7
Pirotta E Booth CG Costa DP Fleishman E Kraus SD Lusseau Det al 2018 Understanding the population consequences of disturbanceEcol Evol 8 9934ndash9946 doi101002ece34458 PMID30386587
Polfus JL Heinemeyer K and Hebblewhite M 2014 Comparing tradi-tional ecological knowledge and western science woodland caribou habi-tat models J Wildl Manage 78 112ndash121 doi101002jwmg643
Roumldder D Nekum S Cord AF and Engler JO 2016 Coupling satellitedata with species distribution and connectivity models as a tool for envi-ronmental management and planning in matrix-sensitive species Envi-ron Manage 58 130ndash143 doi101007s00267-016-0698-y PMID27094442
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Salice CJ Rowe CL Pechmann JHK and Hopkins WA 2011 Multiplestressors and complex life cycles Insights from a population-level assess-ment of breeding site contamination and terrestrial habitat loss in anamphibian Environ Toxicol Chem 30(12) 2874ndash2882 doi101002etc680PMID21922532
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Mahon and Pelech 223
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Smit B and Spaling H 1995 Methods for cumulative effects assessmentEnviron Impact Assess Rev 15 81ndash106 doi1010160195-9255(94)00027-X
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Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
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Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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Smit B and Spaling H 1995 Methods for cumulative effects assessmentEnviron Impact Assess Rev 15 81ndash106 doi1010160195-9255(94)00027-X
Soacutelymos P Matsuoka SM Bayne EM Lele SR Fontaine P Cumming SGet al 2013 Calibrating indices of avian density from non-standardized surveydata making the most of a messy situation Methods Ecol Evol 4 1047ndash1058doi1011112041-210X12106
Soacutelymos P Mahon CL Fontaine T and Bayne EM 2015 Predictive mod-els for estimating the cumulative effects of human development on mi-gratory landbirds in the oil sands areas of Alberta Joint Oil SandsMonitoring Cause-Effects Assessment of Oil Sands Activity on MigratoryLandbirds doi105281zenodo2434067
Spiegelhalter DJ Dawid AP Lauritzen SL and Cowell RG 1993 Bayesiananalysis in expert systems Statist Sci 8 219ndash283 doi101214ss1177010888
Steventon JD Sutherland GD and Arcese P 2006 A population-viability-based risk assessment of Marbled Murrelet nesting habitat policy in BritishColumbia Can J For Res 36(12) 3075ndash3086 doi101139x06-198
Stralberg D Wang X Parisien MA Robinne FN Soacutelymos P Mahon CLet al 2018 Wildfire-mediated vegetation change in boreal forests of AlbertaCanada Ecosphere 9 e02156 doi101002ecs22156
Sutherland GD Waterhouse FL Smith J Saunders SC Paige K andMalt J 2016 Developing a systematic simulation-based approach forselecting indicators in strategic cumulative effects assessments with mul-tiple environmental valued components Ecol Indic 61 512ndash525 doi101016jecolind201510004
Swor T and Canter L 2011 Promoting environmental sustainability via anexpert elicitation process Environ Impact Assess Rev 31 506ndash514 doi101016jeiar201101014
Tremblay JA Boulanger Y Cyr D Taylor AR Price DT and St-Laurent MH2018 Harvesting interacts with climate change to affect future habitat qualityof a focal species in eastern Canadarsquos boreal forest PLoS ONE 13 e0191645doi101371journalpone0191645 PMID29414989
US Fish and Wildlife Service (USFWS) 1980 Habitat as a basis for environ-mental assessment 101 ESM US Department of the Interior Fish andWildlife Service Washington DC
US Fish and Wildlife Service (USFWS) 1981 Standards for development ofhabitat suitability models 103 ESM US Department of the Interior Fishand Wildlife Service Washington DC
Usher PJ 2000 Traditional ecological knowledge in environmental assess-ment and management Arctic 53 183ndash193 doi1014430arctic849
Uusitalo L 2007 Advantages and challenges of Bayesian networks in environ-mental modelling Ecol Modell 203 312ndash318 doi101016jecolmodel200611033
Van Wilgenburg S Mahon CL Campbell G McLeod L Campbell MEvans D et al 2020 A cost efficient spatially balanced hierarchical sam-pling design for monitoring boreal birds incorporating access costs andhabitat stratification PLoS ONE 15(6) e0234494 doi101371journalpone0234494 PMID32544173
Westwood AR Olszynski M Fox CH Ford AT Jacob AL Moore JWand Palen WJ 2019 The role of science in contemporary Canadian envi-ronmental decision making the example of environmental assessmentUniv Br Columbia Law Rev 52(1) 243ndash291
Wilson RR Liebezeit JR and Loya WM 2013 Accounting for uncer-tainty in oil and gas development impacts to wildlife in Alaska ConservLett 6 350ndash358 doi101111conl12016
Wintle BA Bekessy SA Venier LA Pearce JL and Chisholm RA 2005Utility of dynamic-landscape metapopulation models for sustainable forestmanagement Conserv Biol 19 1930ndash1943 doi101111j1523-1739200500276x
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224 Environ Rev Vol 29 2021
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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples 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ltltCompressObjects OffParseDSCCommentsForDocInfo trueCreateJobTicket falsePDFX1aCheck falseColorImageMinResolution 150GrayImageResolution 300DoThumbnails falseColorConversionStrategy LeaveColorUnchangedGrayImageFilter DCTEncodeEmbedAllFonts trueCalRGBProfile (sRGB IEC61966-21)MonoImageMinResolutionPolicy OKImageMemory 1048576LockDistillerParams trueAllowPSXObjects trueDownsampleMonoImages truePassThroughJPEGImages trueColorSettingsFile (None)AutoRotatePages PageByPageOptimize trueMonoImageDepth -1ParseDSCComments trueAntiAliasGrayImages falseGrayImageMinResolutionPolicy OKJPEG2000ColorImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtConvertImagesToIndexed trueMaxSubsetPct 99Binding LeftPreserveDICMYKValues falseGrayImageMinDownsampleDepth 2MonoImageMinResolution 1200sRGBProfile (sRGB IEC61966-21)AntiAliasColorImages falseGrayImageDepth -1PreserveFlatness trueCompressPages trueGrayImageMinResolution 150CalCMYKProfile (US Web Coated 050SWOP051 v2)PDFXBleedBoxToTrimBoxOffset 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256Quality 15TileWidth 256gtgtPDFXOutputIntentProfile ()JPEG2000ColorACSImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtEmbedJobOptions trueMonoImageDownsampleType AverageDetectBlends trueEncodeGrayImages trueColorImageDownsampleType AverageEmitDSCWarnings falseAutoFilterColorImages trueDownsampleGrayImages trueGrayImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtAntiAliasMonoImages falseGrayImageAutoFilterStrategy JPEGGrayACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtColorImageAutoFilterStrategy JPEGColorImageMinResolutionPolicy OKColorImageResolution 300PDFXRegistryName ()MonoImageFilter CCITTFaxEncodeCalGrayProfile (Gray Gamma 22)ColorImageMinDownsampleDepth 1JPEG2000GrayImageDict ltltTileHeight 256Quality 15TileWidth 256gtgtColorImageDepth -1DetectCurves 01PDFXTrapped FalseColorImageFilter DCTEncodeTransferFunctionInfo PreservePDFX3Check falseParseICCProfilesInComments trueColorACSImageDict ltltHSamples [10101010]QFactor 015VSamples [10101010]gtgtDSCReportingLevel 0PDFXOutputConditionIdentifier ()PDFXCompliantPDFOnly falseAllowTransparency falsePreserveCopyPage trueUsePrologue falseStartPage 1MonoImageDownsampleThreshold 10GrayImageDownsampleThreshold 10CheckCompliance [None]CreateJDFFile falsePDFXSetBleedBoxToMediaBox trueEmbedOpenType falseOPM 0PreserveOverprintSettings falseUCRandBGInfo RemoveColorImageDownsampleThreshold 10MonoImageDict ltltK -1gtgtGrayImageDownsampleType AverageDescription ltltENU (Use these settings to create Adobe PDF documents suitable for reliable viewing and printing of business documents Created PDF documents can be opened with Acrobat and Adobe Reader 50 and later)PTB 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