Biological Conservation xxx (2012) xxx–xxx

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Biological Conservation

journal homepage: www.elsevier .com/ locate /biocon

Determining the ecological value of landscapes beyond protected areas

K.J. Willis a,b,⇑, E.S. Jeffers a, C. Tovar a, P.R. Long a, N. Caithness c, M.G.D. Smit d, R. Hagemann d,C. Collin-Hansen d, J. Weissenberger d

a Biodiversity Institute, Oxford Martin School, Department of Zoology, University of Oxford, Oxford OX1 3PS, UKb Department of Biology, University of Bergen, Allégaten 41, N-5007 Bergen, Norwayc Oxford e-Research Centre, University of Oxford, Oxford OX1 3QG, UKd Statoil ASA, Forusbeen 50, 4035 Stavanger, Norway

a r t i c l e i n f o

Article history:Received 18 April 2011Received in revised form 30 October 2011Accepted 2 November 2011Available online xxxx

Keywords:Biodiversity valuationConnectivityEcological footprintFragmentationThreatened speciesResilience

0006-3207/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.biocon.2011.11.001

⇑ Corresponding author at: Biodiversity Institute, Oment of Zoology, University of Oxford, Oxford OX1 3P

E-mail address: Kathy.willis@zoo.ox.ac.uk (K.J. Wil

Please cite this article in press as: Willis, K.J.doi:10.1016/j.biocon.2011.11.001

a b s t r a c t

Whilst there are a number of mapping methods available for determining important areas for conserva-tion within protected areas, there are few tools available for assessing the ecological value of landscapesthat are ‘beyond the reserves’. A systematic tool for determining the ecological value of landscapes out-side of protected areas could be relevant to any development that results in a parcel of land being trans-formed from its ‘natural’ state to an alternative state (e.g., industrial, agricultural). Specifically what isneeded is a method to determine which landscapes beyond protected areas are important for the ecolog-ical processes that they support and the threatened and vulnerable species that they contain. This paperpresents the results of a project to develop a method for mapping ecologically important landscapesbeyond protected areas; a Local Ecological Footprinting Tool (LEFT). The method uses existing globallyavailable web-based databases and models to provide an ecological score based on five key ecological fea-tures (biodiversity, vulnerability, fragmentation, connectivity and resilience) for every 300 m parcelwithin a given region. The end product is a map indicating ecological value across the landscape. Wedemonstrate the potential of this method through its application to three study regions in Canada, Algeriaand the Russian Federation. The primary audience of this tool are those practitioners involved in planningthe location of any landscape scale industrial/business or urban (e.g., new town) facility outside of pro-tected areas. It provides a pre-planning tool, for use before undertaking a more costly field-based envi-ronmental impact assessment, and quickly highlights areas of high ecological value to avoid in thelocation of facilities.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Protected areas have long been the mainstay of biodiversityconservation with 12% of land currently under some form of pro-tection (Jenkins and Joppa, 2009) and a commitment to increasethis to 15–20% by 2020 (Stokstad, 2010). There are a number ofexcellent tools available for mapping conservation priorities withinthese protected landscapes (e.g., C-Plan, Pressey et al., 2009; Mar-xan, Ball et al., 2009; and Zonation, Moilanen, 2007). However,users of the outputs of systematic conservation planning tools havetraditionally treated land outside of the protected area network as‘‘scorched earth’’, i.e., as providing no benefit to biodiversity con-servation (Edwards et al., 2010).

Landscapes beyond protected areas are increasingly beingrecognised as important for providing ecological and evolutionaryprocesses essential for the long-term persistence of biodiversity

ll rights reserved.

xford Martin School, Depart-S, UK. Tel.: +44 1865 281321.lis).

, et al. Determining the ecolog

both within and beyond protected areas (Bengtsson et al., 2003;Carroll et al., 2010; Chazdon et al., 2009; Mathur and Sinha,2008). Key biotic and abiotic features of these landscapes includetheir role in the provision of corridors between reserves (e.g.,through waterways, wetlands, Edwards et al., 2010) and as refugesfor species given future range-shifts resulting from climate change(e.g., Carroll et al., 2010). It is also recognised that many threatenedand protected species have significant populations outside of pro-tected areas (IUCN, 2009) and that the majority of species migra-tion routes occur beyond protected areas (Riede, 2004).Consideration is also needed of the landscape scale features thatare important to maintain resilient ecosystems (Bengtsson et al.,2003; Klein et al., 2009). These biotic and abiotic features havenumerous different spatial configurations across the landscape.

All landscapes outside of the protected areas are not, therefore,equal in terms of ecological value. This point is particularly rele-vant when considering placement of business facilities, industrialoperations and even the building of solar and wind farms; wherecan they be built that will have least ecological impact? Thereare currently very few tools available for mapping conservation

ical value of landscapes beyond protected areas. Biol. Conserv. (2012),

2 K.J. Willis et al. / Biological Conservation xxx (2012) xxx–xxx

priorities outside of reserves. Those that are available tend to be re-gion or country-specific (e.g., NatureServe www.naturserve.org),and/or require a high level of data knowledge and input. Addition-ally, there exist tools such as iBat for business (iBatforbusiness.orghttps://www.ibatforbusiness.org/) which facilitate access to accu-rate and up-to-date biodiversity information but this again focuseson key biodiversity areas and legally protected areas and producesmapped output at a spatial scale (�20 km) that is often too coarsefor most landscape planning decisions.

Currently the mainstays of ecological assessment by businessesfor landscapes beyond protected areas are site Biodiversity ActionPlans and Environmental Impact Assessments (EIA) (Slootweg,2009). These provide the framework by which companies endeav-our to minimise the impacts of their activities on ecosystems andhabitats and ensure that planned development activities are in linewith regional and national Biodiversity Action Plans (http://www.businessandbiodiversity.org/action_site_bap.html). Typi-cally, these plans are based on detailed site surveys carried outby a team of consultants. Whilst these assessments yield an esti-mate of the species present in an area proposed for development,they do not consider the overall footprint of the development uponimportant ecological processes on the landscape. They also tend tooccur once a decision has been made regarding where to develop.The options to relocate a facility due to impact on ecological pro-cesses at this point in the decision making process are also extre-mely limited; rather the EIA often gives an indication ofnecessary mitigation measures to minimise the damage that willbe caused by the development.

The aim of this project therefore was to devise a simple andquick, yet effective method for determination of important ecolog-ical features and processes on landscapes beyond protected areas.In addition, we set out to devise a method that would have a sim-ple user input, would use existing globally available databases,models and algorithms to produce mapped output at a spatial scalerelevant to most landscape scale planning decisions (<0.5 km).

How to measure ecological value is complex. There are manydifferent metrics to consider at varying spatial and temporal scales.At the landscape scale probably the most common measuresadopted to date are indices of species richness, rarity (Smith andTheberge, 1986), taxonomic uniqueness (Vane Wright et al.,1991) and irreplaceability (Pressey et al., 1994). There is also oftena consideration of the contiguous nature of the habitat supportingthe species. Many studies indicate that a more fragmented patch oflandscape, for example, has less ecological value than a largeundisturbed patch (for recent review see Tjørve, 2010). However,these measures are based on observed biological patterns of con-servation ‘assets’ or ‘actors’ but lack information on the ecologicalprocesses that support ecosystem functioning (Bennett et al.,2009). Recently, several studies that have attempted to includeadditional factors in conservation planning that prioritise accord-ing to the ‘dynamic’ features of the landscape including wetlands,rivers, flyways, large mammal migration paths and/or methods toincorporate areas that appear to be more resilient to environmen-tal perturbations because of the combination of biotic/abiotic fea-tures that they contain (Klein et al., 2009; Edwards et al., 2010). Allthese studies agree, however, that effective planning for ecologicalprocesses requires a multi-criteria assessment approach that incor-porates all of these important measures of ecological integrity (Re-gan et al., 2007) in order to secure both the arenas and the actors(Beier and Brost, 2010).

This paper describes the multi-criteria metrics and databasesthat we analysed and the algorithms developed to create a meth-odology to assess the ecological value of parcels of land across alandscape for any location in the world. It then describes resultsfrom testing this methodology for three case study regions in Can-ada, Algeria and Russia. Underlying assumptions associated with

Please cite this article in press as: Willis, K.J., et al. Determining the ecologdoi:10.1016/j.biocon.2011.11.001

this method include: (1) that it should be based on multiple valu-ation factors, (2) make use of free spatial data available for almostany location worldwide, and (3) be at a scale that is relevant to theextent of most development concessions, ideally <0.5 km. The fo-cus here was to determine what freely available spatial data fromthe internet, combined with well-established models and algo-rithms could be used to provide a baseline of ecological informa-tion for any site in the world.

2. Study areas

Three study areas were chosen to determine an ecological valu-ation of the landscape: one in Alberta, Canada (55�4905.519400N,111�27013.319400W); one in central Algeria (27�19033.2400N,2�28044.759400E) and one on the Yamal Peninsula in Russia(72�18047.1600N, 72�26058.200E). These areas were selected to repre-sent a wide variety of eco-regions at different geographical loca-tions and varying data availabilities.

The Alberta site is located within the mid-continental Canadianforest eco-region, which is characterised by continuous mid-borealmixed coniferous and deciduous forest across a variable topogra-phy with discontinuous permafrost cover (http://www.worldwild-life.org/science/ecoregions/item1847.html). Current land uses inthe eco-region include forestry and oil and gas development. Thereis one protected area, Crow Lake, which is 40 km to the west of thestudy area (UNEP-WCMC, 2010).

The Algeria site is located within the Saharan Desert eco-regionwhich is hyper-arid, has minimal perennial vegetation cover andlow species richness and endemism. Despite the extreme condi-tions, it contains a relatively large number of species that areadapted to this high stress environment (http://www.worldwild-life.org/science/ecoregions/item1847.html). There are 70 mamma-lian, 90 avian and 100 reptilian species known to reside within theSaharan Desert eco-region (http://www.worldwildlife.org/science/ecoregions/item1847.html). Oases are also present in the land-scape, where perennial vegetation and agriculture is present. Be-yond the oases, the sparse vegetation is highly adapted to thehyper-arid climate via the persistence of the seed bank that allowsfor masting during high rainfall years. Current land use is domi-nated by nomadic pastoralism. The nearest protected area to thestudy area is the Tassilin’Ajjer National Park, which is also desig-nated as a World Heritage Site and UNESCO Biosphere Reserve(UNEP-WCMC, 2010); this park is about 300 km to the southeastof the study area.

The Yamal Peninsula Russia study area is located within the Ya-mal-Gydan Tundra ecoregion, which is a lowland tundra situatedupon a thick layer of permafrost (http://www.worldwildlife.org/science/ecoregions/item1847.html). Vegetation cover in the north-ern part of the peninsula is dominated by mosses and lichens witha few shrubs and grasses that support larger herds of reindeer dur-ing the summer months. Current land use is dominated by noma-dic pastoralism. The Yamal’skiy marine protected area (UNEP-WCMC, 2010) overlays the study area and extends to the southfor 300 km.

3. Methods

3.1. Identification of data sources and models to determine ecologicalcriteria

Through a series of workshops with practitioners and a detailedliterature review, five criteria were determined as being of primaryimportance to the ecological valuation of a landscape, namely: bio-diversity, vulnerability, fragmentation, connectivity and resilience.These criteria represent both the current ecological properties of

ical value of landscapes beyond protected areas. Biol. Conserv. (2012),

K.J. Willis et al. / Biological Conservation xxx (2012) xxx–xxx 3

the landscape (e.g., biodiversity, threatened species) and the keyfeatures important for supporting ecosystem functions (e.g., con-nectivity (migration routes, wetlands, corridors), habitat integrity,and resilience. Data and models (described below) were thenassimilated to calculate these criteria and an algorithm developedto sum the criteria into an overall dimensionless indicator of eco-logical value to enable a mapped output (see Table 1).

To determine the finest spatial resolution possible for determin-ing an ecological value of a landscape we examined the range ofreadily available, spatially continuous geographic data; global datawere available at every 90 m sized pixel (i.e., one picture elementin a raster or remotely sensed image) for elevation (topography)to 10 km sized pixels for soil characteristics. Given how importanta knowledge of the vegetation cover is for calculation of many ofthe variables (biodiversity, fragmentation, resilience), we selectedvegetation cover at 300 m pixel size resolution (GLOBCOVER, �ESA GlobCover Project, led by MEDIAS-France) as the base layerfor the LEFT; therefore, the study area is divided into 300 m sizedpixels and the other data layers are superimposed upon this in or-der to calculate values for each of the ecological indicators.

3.2. Biodiversity

For most regions in the world there will rarely be enough de-tailed species data to obtain a clear picture of the biodiversity. Itis therefore necessary to model predictive diversity across thelandscape using a combination of point species occurrences andenvironmental variables to predict diversity on landscape (Hirzelet al., 2002; Ferrier et al., 2002; Ferrier and Guisan, 2006). In orderto determine a measure of species occurrence in the study area, weused the data contained in the Global Biodiversity InformationFacility database (GBIF, http://data.gbif.org). The GBIF Data Portalcurrently provides access to more than 300 million records ofspecies occurrence worldwide. It also contains fossil records butthese were removed from the analyses. Data for the abiotic

Table 1Factors to be included in a local ecological footprint tool for assessing ecological integrity

Factor Definition

Biodiversity Compositional turnover with respect to environmental covariatesVulnerability The number of threatened species presentFragmentation The size of the vegetation patchConnectivity River, lake and wetland features. The number of migratory species

presentResilience The ability to sustain high rates of net primary productivity in areas

low precipitation

a Global Biodiversity Information Facility (http://www.gbif.org/).b Lehner and Döll (2004)c Lehner et al. (2008)d Hijmans et al. (2005).e IUCN, 2009.f � ESA GlobCover Project, led by MEDIAS-France.g Riede et al. (2001).h Zhao et al. (2009).

Table 2Environmental covariates used in generalised dissimilarity modelling per biological group

Biological group Predictor variables

Amphibians, birds, mammals andreptiles

Distance to water bodies,a annual mean temperseasonality

Plants Annual mean temperature, total annual precipiwater holding capacity

a Global Lakes and Wetlands Database and HYDROSHEDS.b BIOCLIM.c Harmonized World Soil Database.

Please cite this article in press as: Willis, K.J., et al. Determining the ecologdoi:10.1016/j.biocon.2011.11.001

variables in study area (climate, soils, hydrosheds) were obtainedfrom other global databases (see Table 2).

These data were then used in a Generalised Dissimilarity Modelto determine the compositional turnover in the study area which isthe rate of change in species composition with respect to the envi-ronmental variables (Ferrier et al., 2002). Output from this modelhighlights areas that are more different to their neighbours thanother areas within the landscape in terms of their species assem-blage and thus provides a measure of heterogeneity per unit area.Ideally we would use species richness as well as compositionalturnover to estimate diversity across a landscape using a approachsuch as ecological niche factor analysis (Hirzel et al., 2002). Thismethod predicts habitat suitability for individual species basedon occurrence records and environmental covariates. Species rich-ness is then estimated as the sum of species for which habitat suit-ability exceeds a given threshold. However, to estimate speciesrichness using such models requires more than around 30 recordsper species (Zaniewski et al., 2002). We found that there are veryfew regions in the world that have appropriate densities of GBIF re-cords to allow this approach. Since GDMs evaluate assemblages ofspecies, there is no minimum requirement for the number of spe-cies occurrences; however, we set a minimum number of speciesper biological group (e.g., birds, plants, etc.) at 10 for inclusion ofeach group in the modelling in order to be able to robustly para-meterise a GDM for that group.

Biodiversity data were obtained for a larger area (i.e.,700 km � 700 km) around our study areas for constructing theGDM, and it was these data which were used to generate predictedcompositional dissimilarity for our study area. The biodiversitydata were filtered to retain only those occurrences recorded inlocations within the same eco-regions (Olson et al., 2004) as con-tained in the study area and to remove any duplicate records ofspecies occurrence in one location. GDM predictions of composi-tional dissimilarity for every possible pair of sites within the studyarea were made in the statistical software package R using the

across a landscape and the available data source.

Data source

GBIFa, BIOCLIMd

IUCN Red Liste

GLOBCOVERf

Global Lakes and Wetlands Databaseb, HYDROSHEDSc and Global Registry ofMigratory Speciesg

of MODIS/TERRA NPP Yearly L4 Global 1 km SIN Grid V004h and BIOCLIM

.

ature,b total annual precipitation, temperature seasonality, precipitation

tation, temperature seasonality, precipitation seasonality, % nitrogen in soil,c soil

ical value of landscapes beyond protected areas. Biol. Conserv. (2012),

4 K.J. Willis et al. / Biological Conservation xxx (2012) xxx–xxx

GDM package (Ferrier et al., 2007) and the results were importedinto MATLAB where a distance weighted average (using the inversesquare distance measure) of all pair-wise combinations was calcu-lated per site. The output was transferred back into a GIS platformfor visualisation. Where multiple biological groups were modelled,we retained the highest value of compositional dissimilarity perpixel in order to report the highest potential value of compositionaldissimilarity which could occur in that pixel. Pixels demonstratinghigher levels of compositional dissimilarity were treated as havinga higher ecological value.

3.3. Vulnerability

In order to assess vulnerability of an area in terms of the poten-tial loss of important species if the area was damaged, we used theIUCN Red List of Threatened Species (IUCN, 2009). The 2010 IUCNRed List of Threatened Species has assessments for�56,000 speciesglobally, of which about 28,000 have spatial data (http://www.iucnredlist.org/). Range polygons of globally threatened(CR, EN, VU, or NT) terrestrial vertebrate within each study area(birds, mammals, reptiles and amphibians) were obtained andoverlain in order to count the number of globally threatened ter-restrial vertebrates potentially present in each pixel. While it ispossible to filter or weight species by their threat category, we re-tained all globally threatened species to maintain the breadth ofspecies potentially present in our study areas. Pixels with moreglobally threatened species potentially present were treated hereas having a higher ecological value.

3.4. Fragmentation

There is a vast ecological literature demonstrating the ecologi-cal importance of habitat integrity and ‘intactness’ and the impactof fragmentation on biodiversity (see Fahrig (2003) for review). Ingeneral the greater the patch size, the higher its functionality(greater diversity, more pollinators, greater complexity in foodwebs etc.). A key feature to try and retain on any landscape, there-fore, is habitat integrity. We calculated patch size according to sim-ilar vegetation type based on the classes in the GLOBCOVERdataset. The software FRAGSTATS (McGarigal et al., 2002) was usedto define a patch using the rule of eight pixels (orthogonal anddiagonal adjacency) and to estimate the natural logarithm of thearea of each patch (ha). Prior to running FRAGSTATS, we reclassi-fied the GLOBCOVER vegetation categories into the followinggroups: closed forest, open forest, forest/shrub/grass mosaic, bareareas, water/snow/ice. Larger areas of continuous, similar vegeta-tion cover were assumed to have a higher ecological value. Areascharacterised by human activities, lakes, non-natural and bareareas were automatically set a value of zero for patch size.

3.5. Connectivity

Connectivity across a landscape either through riverine corri-dors and/or other migratory routes is essential to any ecologicallyfunctioning landscape. We therefore include a calculation to prior-itize pixels that support migratory processes. Two complimentaryfactors that represent migration processes across the landscapewere included namely (i) migration routes as identified in the Glo-bal Register of Migratory Species GROMS (www.groms.de) and (ii)waterways and wetlands. GROMS currently contains a list of 2880migratory vertebrate species in digital format and digital maps for545 of these species. It therefore provides a large number of poly-gons of migratory ranges and distributions of species that migrateacross national boundaries (Riede and Kunz, 2001). We used theGROMS maps in the same way as the IUCN polygons: we calculatedthe number of migratory species ranges which intersect each pixel

Please cite this article in press as: Willis, K.J., et al. Determining the ecologdoi:10.1016/j.biocon.2011.11.001

and those pixels that had the highest number of migration routesoccurring across them were deemed to be of higher ecologicalvalue.

To determine the pixels that supported river corridors and/orwetlands (and the pixels immediately adjacent to these) we useddata available in the HYDROSHEDS 15-arc-second river networkdatabase (hydrosheds.wwf) (Lehner et al., 2008) and the GlobalLakes and Wetlands database GLWD3 wetland classification(Lehner and Döll, 2004). Pixels which were situated within ariverine corridor or wetland were given a higher ecological score.

3.6. Resilience

Ecological resilience is the capacity of a system to undergo dis-turbance and maintain its functions and controls (Holling, 1973;Folke et al., 2004). Resilience is therefore an important ecologicalfeature of any landscape and areas that can maintain resilience de-spite climate/environmental disturbance are of high ecological sig-nificance. To determine climatic resilience we adopted theapproach used by Klein et al. (2009) that focuses on the ability ofa parcel of land to maintain productivity – a fundamental aspectof ecosystem functioning – despite relatively high water stress.Values of annualized net primary productivity (NPP, Zhao et al.,2009) per vegetation type (determined by GLOBCOVER) were over-laid with data of the typical precipitation of the driest quarter(WORLDCLIM bio17, Hijmans et al., 2005) to identify patternsacross space in the level of productivity of each vegetation type gi-ven spatial variations in rainfall. To ensure that NPP data were con-sistent with the vegetation cover data, we used total NPP in theyear 2005 since this was the year in which the GLOBCOVER datawas created. We calculated quartiles for the precipitation andNPP data per vegetation type within the study area, then appliedthe following rules for assessing the resilience value of each pixel:a value of 1 (i.e., the highest resilience) was assigned to pixels thatwere in both the upper quartile of NPP and the lower quartile ofrainfall; a value of 0.5 was assigned to pixels that were in the upperquartile of productivity and the second lowest quartile of rainfall;all other pixels were given a value of zero (i.e., the lowest value) forresilience. All bare areas, lakes and agricultural areas were as-signed a value of zero. The resulting index highlights the pixelscontaining the most resilient patches of vegetation based on theability to retain productivity despite low rainfall. Ideally a measureof resilience to environmental disturbances (e.g., burning, soil ero-sion) should also be calculated. However, as yet no global datasetsare yet available that can be harnessed to produce a relative mea-sure of resilience to such environmental disturbances across space.

3.7. Summary ecological value

In order to ascribe a dimensionless ecological value to each300 m pixel that incorporates all five measures described above,we standardised the values of each factor to an index between zeroand one. This was achieved by setting the minimum value to zeroand dividing the individual values per pixel by the maximum valueof each measure. The biodiversity, connectivity (rivers, lakes andwetlands) and resilience measures were already in binary integervalues and thus required no conversion. After standardisation, wesummed the value of each factor together to provide an overallindication of ecological value per pixel according to the formula:Summary ecological value = Biodiversity + Vulnerability + Frag-mentation + Connectivity + Resilience. The maximum ecologicalvalue for any pixel was therefore six given that there are two com-plementary measures of connectivity; however, the actual maxi-mum value is dependent on the availability of the data for eachstudy site.

ical value of landscapes beyond protected areas. Biol. Conserv. (2012),

K.J. Willis et al. / Biological Conservation xxx (2012) xxx–xxx 5

4. Data handling and display

For the study areas described above, we manually downloadedall of the spatial data and projected it into the Universal TransverseMercator (UTM) coordinate system. We set the study area at100 km � 100 km and each 300 m pixel within this area was eval-uated for its ecological value using the algorithm described above.Once calculated, the summary ecological value for each pixel wasplotted on a final map layer ArcGIS 10 to display differences in va-lue across the landscape.

5. Results

5.1. Canada

The Globcover map for the study region is presented in Fig. 1.Output from GBIF indicates that in this region there are pointoccurrences for birds (n = 282 species), mammals (n = 18 species)amphibians (n = 10 species) and plants (n = 545 species). Usingthis data in combination with the environmental co-variates (Ta-ble 2) the GDM output demonstrates that predicted biodiversityis the highest in the northwestern portion of the study area, inpixels that contain closed needle-leaved evergreen forest andlakes (Supplementary data Fig. 1a). The IUCN threatened specieslist indicates that the study area contains two globally threa-tened species both of which potentially occur across the wholestudy area (Supplementary data Fig. 1b). In terms of habitat frag-mentation, results from FRAGSTATS reveals that there is a widerange in vegetation patch size in this study area from less than1 ha to over 1000 ha (Supplementary data Fig. 1c). The largestcontinuous patch consisting of closed needle-leaved deciduousand evergreen forest occupies the central region of the studyarea. This large patch is surrounded by much smaller patchesof a mosaic of forest, shrubland and grasslands. In terms of con-nectivity, within the study area there are predicted to be atleast59 internationally migratory species and the number in anylocation ranges from 52–59 (Supplementary data Fig. 1d). The

Fig. 1. Alberta, Cana

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greatest number of international migratory species appears tobe concentrated in the north central part of the large continuouspatch of closed needle-leaved forest. This area is marked by adense river network and both small and large wetlands areassociated with these rivers (Supplementary data Fig. 1e). Theresilience measure indicated that the areas of highest resilienceare in the north-west corner of the landscape (Supplementarydata Fig. 1f). Other areas of high resilience are scattered aroundthe study area, especially in the east where there are numerouswetlands.

The summary ecological valuation map is presented in Fig. 2.This demonstrates that the highest possible ecological value forthis study area is 6 (values assigned for biodiversity, vulnerability,fragmentation, connectivity (due to migratory species plus thelandscape features that support migration) and resilience. Thehighest values (darkest grey-scale) appear to be consistent withthe river boundaries, with large areas of continuous boreal forestand with some smaller areas of wetland along the western edgeof the study area.

5.2. Algeria

The Globcover map for the study region is presented in Fig. 3. Adesert region with sparse vegetation cover except for that presenton intermittent wetlands (oases) in the centre of the area. GBIFspecies occurrences for the Algeria site indicate that only bird spe-cies can be used in the GDM modelling (n = 27) since there werenot enough data for mammals (n = 7), plants (n = 1), reptiles(n = 3) or amphibians (n = 0). Output from the GDM modellingusing the bird data combined with environmental covariates (Ta-ble 2) indicated highest beta diversity in the central and south-western portions of the study region, which is associated in spacewith the boundary between unconsolidated bare areas (i.e., sanddunes) and consolidated bare areas (Supplementary data Fig. 2a).

In terms of the vulnerability measure, data from the IUCN RedData list of Threatened species indicates that there three differentglobally threatened species predicted to occur within the study

da study area.

ical value of landscapes beyond protected areas. Biol. Conserv. (2012),

Fig. 2. Measures of ecological value for the Canadian study area including: (a) beta-diversity, (b) vulnerability, (c) fragmentation, (d) connectivity due to migratory species,(e) connectivity from rivers, lakes and wetlands, (f) vegetation resilience. These layers are overlaid to determine the (g) summary value of each pixel within the Canadianstudy area (see also Supplementary data Fig. 1a–f.)

6 K.J. Willis et al. / Biological Conservation xxx (2012) xxx–xxx

region. All three occur in the northern portion of the study area,where the land cover is primarily consolidated bare areas (Supple-mentary data Fig. 2b).

As this region is part of the Saharan desert, there is little to novegetation cover within the study area, thus the only areas subjectto the fragmentation assessment is the tiny patch of vegetation inthe centre of the study area (Supplementary data Fig. 2c). This veg-etation cover is so sparse that the maximum patch size is about0.03 ha. This area is surrounded by largely continuous desert landcover. Output from GROMS indicates that there are at least eightinternationally migrating species that are expected to utilise thehabitat around the Algerian study area (Supplementary dataFig. 2d). The predicted range of these species appears to be concen-trated in the northwestern region of the study area, which is deserthabitat and far away from human settlements that tend to concen-trate around the wetland areas. Despite the hyper-aridity of thearea, there are intermittent wetlands (i.e., oases) within the studyarea that may provide support for the migratory bird species thatare known to occur in the Tasslin’Ajjer National Park and these pix-els are detected in the connectivity layer (Supplementary dataFig. 2e).

It was not possible to assess the differences in resilience of veg-etation across space for this area (Supplementary data Fig. 2f). Thisis due to the extreme aridity of the Saharan Desert and theresulting high variability in vegetation cover over each year result-ing in no consistency between the NPP data and vegetation cover.

The summary ecological value for the Algerian study area is pre-sented in Fig. 4 and is based on biodiversity, vulnerability, frag-mentation and both indicators of connectivity; therefore, themaximum value of the summary ecological value for this site is5. The resulting map indicates that the majority of the study areahas low relative ecological value (Fig. 4) while there are small patchesof higher ecological value at the sites of the intermittent wetlands(in the northeast and southwest) and around the few patches ofvegetation cover (central area). Another region of high ecological

Please cite this article in press as: Willis, K.J., et al. Determining the ecologdoi:10.1016/j.biocon.2011.11.001

value is the northern boundary of the study area, which is consis-tent with the highest values of connectivity and vulnerability.

5.3. Russia

The Globcover map for the Yamal peninsula study region ispresented in Fig. 5. Results from GBIF indicate a serious data gapof species occurrences for this region with only four mammal spe-cies and eight plant species recorded (Biodiversity Data from GBIF).It was not therefore possible to calculate a measure of betadiversi-ty for this region.

In terms of a vulnerability calculation, the IUCN Red data list forThreatened Species indicates that there are two globally threa-tened terrestrial vertebrates that are predicted to occur in thestudy area (Supplementary data Fig. 3b). Results from the fragmen-tation calculation using FRAGSTATS was good and indicated varia-tion in patch sizes ranging from <1 ha to 3200 ha (Supplementarydata Fig. 3c). The largest patch is found in the southern part of thestudy area, a landscape that is predominantly covered by grass-lands and shrublands and interspersed with small water bodies.In the northern part of the study area there are also numerouspatches of regularly flooded vegetation and open forest, includingboth deciduous and evergreen forest.

A measure of connectivity was also possible for this site;GROMs indicates that there are at least 21 migratory animals pre-dicted to occur in the study area; there was little spatial differencein their migratory ranges. (Supplementary data Figs. 3d and 3e)The resilience measure (Supplementary data Fig. 3f) indicated apatchy distribution of resilient vegetation across the landscape.The summary ecological value map (Fig. 6) for the Russian studyarea is therefore based on 5 of the possible 6 layers: vulnerability,fragmentation, two connectivity measures and resilience (i.e., themaximum value is five). Results from this map indicate the areasof highest ecological value are along the coastline and along theriver and wetland networks (Fig. 6).

ical value of landscapes beyond protected areas. Biol. Conserv. (2012),

Fig. 4. Measures of ecological value for the Algerian study area including: (a) beta-diversity, (b) vulnerability, (c) fragmentation, (d) connectivity due to migratory species,(e) connectivity from rivers, lakes and wetlands, (f) vegetation resilience. These layers are overlaid to determine the (g) summary ecological value of each pixel within theAlgerian study area (see also Supplementary data Fig. 2a–f.)

Fig. 3. Algeria study area.

K.J. Willis et al. / Biological Conservation xxx (2012) xxx–xxx 7

6. Discussion

For all three study areas examined, using globally available dat-abases and existing models and algorithms, this study demon-strates that it is possible to obtain an estimation of the spatialdistribution of some important ecological features across a

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landscape and at a relatively fine spatial resolution (here at a300 m pixel resolution). On a global scale, some data gaps are inev-itable, but using a multi-criteria metric approach such as describedhere, it would appear that even for data poor regions, there is nowsufficient information available in global databases to make someestimation of ecological value. The poorest data-region from our

ical value of landscapes beyond protected areas. Biol. Conserv. (2012),

Fig. 6. Measures of ecological value for the Russian study area including: (a) beta-diversity, (b) vulnerability, (c) fragmentation, (d) connectivity due to migratory species, (e)connectivity from rivers, lakes and wetlands, (f) vegetation resilience. These layers are overlaid to determine and (g) summary ecological value of each pixel within theRussian study area (see also Supplementary data Fig. 3a–f).

Fig. 5. Russia study area.

8 K.J. Willis et al. / Biological Conservation xxx (2012) xxx–xxx

three case-studies was northern Russia – but it was still possible toobtain spatial information sufficient to map vulnerability, frag-mentation and connectivity across this landscape; information thatcould have relevance to sighting of industrial facilities and high-light areas of high ecological risk.

Please cite this article in press as: Willis, K.J., et al. Determining the ecologdoi:10.1016/j.biocon.2011.11.001

In addition to the data gaps however, there are also sources ofuncertainty associated with these global databases which need tobe acknowledged from the outset. The main limitation is the coarseresolution of some of the input data, especially from the speciesdistribution ranges (Palminteri et al., 2011). The difference

ical value of landscapes beyond protected areas. Biol. Conserv. (2012),

K.J. Willis et al. / Biological Conservation xxx (2012) xxx–xxx 9

between predicted distributions and actual occurrences of the speciescan only be properly detected by ground-truthing the data. How-ever, this methodology is such that additional data can be incorpo-rated if and when they are available. The GLOBCOVER informationhas a known accuracy level of 79.25% (Bicheron et al., 2009) there-fore ground-truthing can also verify the classification of vegetationand whether vegetation change has occurred since the MERIS dataused in the GLOBCOVER classification were acquired (i.e., since2005). The uncertainty associated with the GBIF points is relatedto the correct estimation of coordinates from the original source;while preliminary checks are routinely conducted on GBIF data, itis essential to ground-truth these species data where possible(Chapman, 2005). In order to obtain a quantitative measure ofuncertainty, therefore, future work plans to include a comparativeanalysis of the output from the approach described herein with theoutput from high-resolution, ground-truthed datasets.

The other question that needs to be addressed is how this newapproach aligns with the other methodologies currently available.As mentioned in the introduction, there is an extensive literatureand an excellent number of tools now available to measure ecolog-ical value for the selection of priority areas for conservation (e.g., C-Plan, Pressey et al., 2009; Marxan, Ball et al., 2009; and Zonation,Moilanen, 2007). Recently several methodologies have also beenproposed for the conservation of landscape features important tomaintaining ecosystem processes (i.e., migratory, (Edwards et al.,2010) and evolutionary, (Klein et al., 2009)). However, these mea-sures and tools have been developed almost exclusively for use instrategic conservation planning exercises. Whilst they can (and in afew cases, have) been used for systematic planning outside of pro-tected areas, their requirement for very high quality species/envi-ronmental data often means that they are of extremely limited usein data poor regions. In addition, since the focus of such approachesis on finding the best location for conservation, less attention is gi-ven to the ecological versus economic tradeoffs; something that isof much higher consideration outside of protected areas. Such atrade-off approach is starting to be considered for some site facilitylocation projects; for example, van Haaren and Fthenakis (2011)have developed a GIS-based site selection tool for locating wind-turbines on the landscape such that important bird areas areavoided. Similarly in restoration projects, spatial decision supporttools are now being developed to inform landscape-scale forestrestoration such that multiple landscape functions (ecologicaland economic) are maximised (Gimona & van der Horst, 2007).

With an increasing requirement to map and value landscapesaccording to their ecosystem service provision, it is inevitable thateconomic/ecological trade-offs will need to be considered more of-ten (de Groot et al., 2010). It is also widely acknowledged that be-fore this can happen, there is a urgent need to devisemethodologies that can map at a landscape scale, the importantecological properties and functions of landscapes, and often forlandscapes where detailed data on species is far from complete.As recently stated in a review on the state of Biodiversity markets(Madsen et al., 2011) ’’a gap in market infrastructure that persistsis lack of landscape-scale ecological monitoring – while site-levelecological monitoring is not uncommon, the data is not easilyavailable, much less compiled in a comprehensive way’’. The ap-proach we have presented in this paper is a first attempt at provid-ing a framework to do this – and by its use of a range of ecologicalindicators, it can both compensate for the data gaps in some layersand also be used to support a wider variety of land-use decisions.

7. Conclusions

Using the approach described, we found that for all three casestudies it was possible to obtain a measure of the ecological value

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of the landscapes under investigation even if for some regions itwas only possible to measure a few indicators. What this method-ology has also demonstrated is that there is a vast amount of globaldata available on the web to provide enough information to dem-onstrate differences in ecological value across the landscape foreven the remotest of study areas, anywhere in the world.

Use of this methodology to analyse the three study areas inev-itably highlighted significant differences in data availability acrossthe globe despite our use of globally available datasets. In particu-lar there was a general lack of freely available remotely sensedenvironmental data in high latitude regions such as in the YamalPeninsula, Russia. To make full use of this approach for such re-gions may require accessing additional sources of remotely senseddata, which are available for a variety of costs but can providehigh-resolution data.

We envisage that this methodology has potential for use in fourstages of planning and facility location in landscapes beyond pro-tected areas as follows:

1. As a pre-planning tool to be used before any site locations aredetermined to highlight the most ecologically sensitive regions.The tool would therefore be run before the EIA and be used as aguide to appropriate areas for more detailed on-ground surveys.

2. To provide additional information for the Environmental ImpactAssessment. Some of the information obtained by this tool willnot be routinely collected as part of the EIA and can therefore beused to provide additional sources of ecological information fora chosen development area. Furthermore, is provides a way ofvisualising the ecological value of the whole EIA study area.

3. To assess long-term ecological impact of a development: there-fore to assess ecological value of the landscape before, duringand post-development to determine ecological improvementor loss over time when combined with field-based, ground-tru-thed data.

4. To provide the first stage of assessment in the determination ofareas for Biodiversity Offsetting projects (Business and Biodi-versity Offsets programme (BBOP, http://bbop.forest-tren-ds.org/).

The next steps in the LEFT development are to automate themethods described herein, which will perform the analysis forany location in the world within a web-based tool and end-userplatform. Additionally, we are undertaking a ground-truthing anduncertainty estimation exercise by comparing the results of ourLEFT methodology with those obtained from high-resolution fielddatasets.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.biocon.2011.11.001.

References

Ball, I.R., Possingham, H.P., Watts, M., 2009. Marxan and relatives Software forspatial conservation optimization. In: Moilanen, A., Wilson, K.A., Possingham,H.P. (Eds.), Spatial Conservation Prioritization Quantitative Methods andComputational Tools. Oxford University Press, Oxford.

Beier, P., Brost, B., 2010. Use of land facets to plan for climate change: conservingthe arenas, not the actors. Conservation Biology 24, 701–710.

Bengtsson, J., Angelstam, P., Elmqvist, T., Emanuelsson, U., Folke, C., Ihse, M.,Moberg, F., Nystrom, M., 2003. Reserves, resilience and dynamic landscapes.Ambio 32, 389–396.

Bennett, A.F., Haslem, A., Cheal, D.C., Clarke, M.F., Jones, R.N., Koehn, J.D., Lake, P.S.,Lumsden, L.F., Lunt, I.D., Mackey, B.G., Mac Nally, R., Menkhorst, P.W., New, T.R.,Newell, G.R., O’Hara, T., Quinn, G.P., Radford, J.Q., Robinson, D., Watson, J.E.M.,Yen, A.L., 2009. Ecological processes: a key element in strategies for natureconservation. Ecological Management & Restoration 10, 192–199.

ical value of landscapes beyond protected areas. Biol. Conserv. (2012),

10 K.J. Willis et al. / Biological Conservation xxx (2012) xxx–xxx

Bicheron, P., Defourny, P., Brockmann, C., Schouten, L., Vancutsem, C., Huc, M.,Bontemps, S., Leroy, M., Achard, F., Herold, M., Ranera, F., Arino, O., 2009.GLOBCOVER: A 300 m global land cover product for 2005 using ENVISAT/MERIStime series. In: Proceedings of the Recent Advances in Quantitative RemoteSensing Symposium, Valencia.

Carroll, C., Dunk, J.R., Moilanen, A., 2010. Optimizing resiliency of reserve networksto climate change: multispecies conservation planning in the Pacific Northwest,USA. Global Change Biology 16, 891–904.

Chapman, A.D., 2005. Principles and methods of data cleaning - Primary species andspecies occurrence data, version 1.0, Copenhagen.

Chazdon, R.L., Harvey, C.A., Komar, O., Griffith, D.M., Ferguson, B.G., Martinez-Ramos, M., Morales, H., Nigh, R., Soto-Pinto, L., van Breugel, M., Philpott, S.M.,2009. Beyond reserves: a research agenda for conserving biodiversity in human-modified Ttopicallandscapes. Biotropica 41, 142–153.

de Groot, R.S., Alkemade, R., Braat, L., Hein, L., Willemen, L., 2010. Challenges inintegrating the concept of ecosystem services and values in landscape planning,management and decision making. Ecological Complexity 7, 260–272.

Edwards, H.J., Elliott, I.A., Pressey, R.L., Mumby, P.J., 2010. Incorporating ontogeneticdispersal, ecological processes and conservation zoning into reserve design.Biological Conservation 143, 457–470.

Fahrig, L., 2003. Effects of habitat fragmentation on biodiveristy. Annual Reviews ofEcology, Evolution and Systematics 34, 487–515.

Ferrier, S., Guisan, A., 2006. Spatial modelling of biodiversity at the communitylevel. Journal of Applied Ecology 43, 393–404.

Ferrier, S., Drielsma, M., Manion, G., Watson, G., 2002. Extended statisticalapproaches to modelling spatial pattern in biodiversity in northeast NewSouth Wales II. Community-level modelling. Biodiversity and Conservation 11,2309–2338.

Ferrier, S., Manion, G., Elith, J., Richardson, K., 2007. Using generalized dissimilaritymodelling to analyse and predict patterns of beta diversity in regionalbiodiversity assessment. Diversity and Distributions 13, 252–264.

Folke, C., Carpenter, S., Walker, B., Scheffer, M., Elmqvist, T., Gunderson, L., Holling,C.S., 2004. Regime shifts, resilience and biodiversity in ecosystem management.Annual Review Ecology, Evolution and Systematics 35, 557–581.

GBIF: Museum of Zoology, University of Navarra; Paleobiology Database;Invertebrates (GBIF-SE:SMNH); Peabody Invertebrate Paleontology DiGIRService; Missouri Botanical Garden; Zoological Museum Amsterdam,University of Amsterdam (NL) – Aves; Western Palearctic migrants incontinental Africa; EURISCO, The European Genetic Resources SearchCatalogue; Bird specimens; SysTax; CGN-PGR; The Collection of LichenicolousFungi at the Botanische Staatssammlung München; Herbarium WU; UnitedStates National Plant Germplasm System Collection; NMNH InvertebrateZoology Collections; FMNH Mammals Collections; Botany (UPS); Jardi Botanicde Valencia: VAL; ZFMK Orthopteroidea collection; Ichtyologie; NMNHVertebrate Zoology Mammals Collections; NSW herbarium collection; ArizonaState University Lichen Collection; Real Jardin Botanico (Madrid), Vascular PlantHerbarium (MA); Mammals (NRM); African Rodentia; SABIF Resource; DBL Life;Peabody Paleoportal DiGIR Service (IP); Staatliches Museum für NaturkundeStuttgart; MAL; Royal British Columbia Museum; Provincial Museum of Alberta,Edmonton, AB, Canada. Birds (Aves); University of Guelph, Department ofEnvironmental Biology; University of New Brunswick Collection; ManitobaMuseum of Man and Nature; Provincial Museum of Alberta; Agriculture andAgri-Food Canada, Saskatoon; Gerald Hilchie Collection; M. Gollop Collection;New Brunswick Museum Collection; Donald F. Hooper Butterfly collection;Nova Scotia Museum of Natural History, Halifax, NS, Canada; Ross A. LayberryObservations; North West Territories and Nunavut Bird Checklist, Canada;University of Saskatchewan; University of Western Ontario Collection; RoyalSaskatchewan Museum Collection; Norbert Kondla Collection; NBM Unionoids;eBird; MCZ Ornithology Collection; Santa Barbara Musem of Natural History;NBM birds; University of Alberta Herpetology Collection; Herbarium (UNA);UAM Botany Specimens; Lund Botanical Museum (LD); University of AlbertaMammalogy Collection; MSB Mammals Specimens; MVZ Mammals Specimens;Lichen herbarium, Bergen (BG); Lichen herbarium, Oslo (O); BiologiezentrumLinz; Lichen Herbarium Berlin; Animal Sound Archive Berlin; ProjectFeederWatch; Herbarium GZU; The Collection of Lichenicolous Fungi at theBotanische Staatssammlung München; Great Backyard Bird Count; AustralianNational Herbarium (CANB); Canadian Lakes Loon Survey; CBS fungi strains;Australian Antarctic Division Herbarium; Canadian Museum of Nature BirdCollection; Canadian Museum of Nature Amphibian and Reptile Collection -Anura; Vascular Plant Collection – University of Washington Herbarium (WTU);Mammal Collection Catalog; FrogWatch Canada; Canadian Museum of Nature –Fish Collection (OBIS Canada); Atlantic Reference Centre (OBIS Canada); CSUHerbarium; Illinois Natural History Survey; Harvard University Herbaria;Peabody Mammalogy DiGIR Service; Phragmites of Canada; CanadianMuseum of Nature Herbarium; MEXU/Colección de Briofitas; USU-UTCSpecimen Database; NSW herbarium collection; Arizona State UniversityLichen Collection; Fairchild Tropical Botanic Garden Virtual HerbariumDarwin Core format; University of Alberta Ornithology Collection; MorandinPhD Thesis/La Crete, Alberta; Botanic Garden of Finnish Museum of NaturalHistory; Peter Hall Observations; NMNH Botany Collections; CONN GBIF data;Botany Vascular Plant Collection; Therevid PEET Project; Herbarium ofOskarshamn (OHN); Lund Botanical Museum (LD); Zoology (Museum ofEvolution – Uppsala); UAM Mammals Specimens; EUNIS; HMAP-History pfMarine Animal Populations (CoML); Senckenberg – CeDAMar Resource;

Please cite this article in press as: Willis, K.J., et al. Determining the ecologdoi:10.1016/j.biocon.2011.11.001

Phanerogamic Botanical Collections (S). Accessed through GBIF Data Portal,www.gbif.org, 2010–7-25.

Gimona, A., van der Horst, D., 2007. Mapping hotspots of multiple landscapefunctions: a case study on farmland afforestation in Scotland. LandscapeEcology 22, 1255–1264.

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very highresolution interpolated climate surfaces for global land areas. InternationalJournal of Climatology 25, 1965–1978.

Hirzel, A.H., Hausser, J., Chessel, D., Perrin, N., 2002. Ecological-niche factor analysis:how to compute habitat-suitability maps without absence data? Ecology 83,2027–2036.

Holling, C.S., 1973. Resilience and stability of ecological systems. Annual Review ofEcology, Evolution and Systematics 4, 1–23.

IUCN, 2009. Red List of Threatened Species. Version 2009.1. (downloaded 21.07.10).Jenkins, C.N., Joppa, L., 2009. Expansion of the global terrestrial protected area

system. Biological Conservation 142, 2166–2174.Klein, C., Wilson, K., Watts, M., Stein, J., Berry, S., Carwardine, J., Smith, M.S., Mackey,

B., Possingham, H., 2009. Incorporating ecological and evolutionary processesinto continental-scale conservation planning. Ecological Applications 19, 206–217.

Lehner, B., Döll, P., 2004. Development and validation of a global database of lakes,reservoirs and wetlands. Journal of Hydrology 296, 1–22.

Lehner, B., Verdin, K., Jarvis, A., 2008. New global hydrography derived fromspaceborne elevation data. EOS Transactions, AGU 89, 93–94.

Madsen, B., Caroll, N., Kandy, D., Bennett, G., 2011. Update: State of BiodiversityMarkets. Washington, DC: Forest Trends, 2011. <http://www.ecosystemmarketplace.com/reports/2011_update_sbdm>.

Mathur, P.K., Sinha, P.R., 2008. Looking beyond protected area networks: a paradigmshift in approach for biodiversity conservation. International Forestry Review10, 305–314.

McGarigal, K., Cushman, S.A., Neel, M.C., Ene, E., 2002. FRAGSTATS: Spatial PatternAnalysis Program for Categorical Maps. Computer Software Program Producedby the Authors at the University of Massachusetts, Amherst. <http://www.umass.edu/landeco/research/fragstats/fragstats.html>.

Moilanen, A., 2007. Landscape Zonation, benefit functions and target-basedplanning: unifying reserve selection strategies. Biological Conservation 134,571–579.

Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G.V.N.,Underwood, E.C., D’Amico, J.A., Itoua, I., Strand, H.E., Morrison, J.C., Loucks, C.J.,Allnutt, T.F., Ricketts, T.H., Kura, Y., Lamoreux, J.F., Wettengel, W.W., Hedao, P.,Kassem, K.R., 2004. Terrestrialecoregions of the world: a new map of life onearth. Bioscience 51, 933–938.

Palminteri, S., Powell, G., Endo, W., Kirkby, C., Yu, D., Peres, C.A., 2011. Usefulness ofspecies range polygons for predicting local primate occurrences in southeasternPeru. American Journal of Primatology 73, 53–61.

Pressey, R.L., Johnson, I.R., Wilson, P.D., 1994. Shades of irreplaceability – towards ameasure of the contribution of sites to a reservation goal. Biodiversity andConservation 3, 242–262.

Pressey, R.L., Watts, M.E., Barnett, T.W., Ridges, M.J., 2009. The C-Plan conservationplanning system: origins, applications and possible futures. In: Moilanen, A.,Wilson, K.A., Possingham, H.P. (Eds.), Spatial Conservation Prioritization:Quantitative Methods and Computational Tools. Oxford University Press,Oxford.

Regan, H.M., Davis, F.W., Andelman, S.J., Widyanata, A., Freese, M., 2007.Comprehensive criteria for biodiversity evaluation in conservation planning.Biodiversity and Conservation 16, 2715–2728.

Riede, K., 2004. Global Register of Migratory Species – from Global to RegionalScales. Final Report of the R&D-Projekt. Federal Agency for Nature Conservation,Bonn, p. 403.

Riede, K., Kunz, K., Germany. Bundesamt fèur Naturschutz., 2001. Global Register ofMigratory Species: Database, GIS Maps and Threat Analysis. Federal Agency forNature Conservation, Bonn, p. 403.

Slootweg, R., 2009. Biodiversity in Environmental Assessment : EnhancingEcosystem Services for Human Well-being. Cambridge University Press,Cambridge, pp. xviii, 437.

Smith, P.G.R., Theberge, J.B., 1986. A review of criteria for evaluating natural areas.Environmental Management 10, 715–734.

Stokstad, E., 2010. Despite progress, biodiversity declines. Science 329, 1272–1273.Tjørve, E., 2010. How to resolve the SLOSS debate: lessons from species-diversity

models. Journal of Theoretical Biology 264, 604–612.Protected Area Data were Obtained from UNEP-WCMC World Database on

Protected Areas. <http://www.protectedplanet.net/>. (accessed 10.09.10).van Haaren, R., Fthenakis, V., 2011. GIS-based wind farm site selection using spatial

multi-criteria analysis (SMCA): evaluating the case for New York State.Renewable &Sustainable Energy Reviews 15, 3332–3340.

Vane Wright, R.I., Humphries, C.J., Williams, P.H., 1991. What to protect –systematics and the agony of choice. Biological Conservation 55, 235–254.

Zaniewski, A.E., Lehmann, A., Overton, J.M.C., 2002. Predicting species spatialdistributions using presence-only data: a case study of native New Zealandferns. Ecological Modelling 157, 261–280.

Zhao, M., Nemani, R., Running, S.W., 2009. MOD17A3.05 MODIS Annual Net PrimaryProductivity at 0.0083 resolution. Acquired for year 2005. ftp://ftp.ntsg.umt.edu/pub/MODIS/Mirror/MOD17A3.305/Improved_MOD17A3_C5.1_GEOTIFF_1km/. (accessed 24.03.11).

ical value of landscapes beyond protected areas. Biol. Conserv. (2012),