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Uncertainty in coarse conservation assessments hindersthe efficient achievement of conservation goals

Virgilio Hermoso ⇑, Mark J. KennardAustralian Rivers Institute and Tropical Rivers and Coastal Knowledge, National Environmental Research Program, Northern Australian Hub, Griffith University, Nathan,Queensland 4111, Australia

a r t i c l e i n f o

Article history:Received 5 October 2011Received in revised form 12 January 2012Accepted 15 January 2012Available online 3 February 2012

Keywords:Commission errorGrain sizeMarxanPriority areaUncertaintyFreshwater fish

a b s t r a c t

Conservation planning is sensitive to a number of scale-related issues, such as the spatial extent of theplanning area, or the size of units of planning. An extensive literature has reported a decline in efficiencyof conservation outputs when planning at small spatial scales or when using large planning units. How-ever, other key issues remain, such as the grain size used to represent the spatial distribution of conser-vation features. Here, we evaluate the effect of grain size of species distribution data versus size ofplanning units on a set of performance measures describing efficiency (ratio of area where species arerepresented/total area needed), rate of commission errors (species erroneously expected to occur), rep-resentativeness (proportion of species achieving the target) and a novel measure of overall conservationuncertainty (integrating commission errors and uncertainty in the actual locations where species occur).We compared priority areas for the conservation of freshwater fish in the Daly River basin (northernAustralia). Our study demonstrates that the effect of grain size of species distribution data was moreimportant than planning unit size on conservation planning performance, with an increase in commis-sion errors up to 80% and conservation uncertainty over 90% when coarse data were used. This was morepronounced for rare than common species, where the mismatch between coarse representations ofbiodiversity patterns and the smaller areas of actual occupancy of species was more evident. Specialattention should be paid to the high risk of misallocation of limited budgets when planning in heteroge-neous or disturbed environments, where biodiversity is patchily distributed, or when planning for con-servation of rare species.

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1. Introduction

Biodiversity conservation usually competes with other humanuses and interests so the identification of priority areas whereconservation management actions can be effectively carried out(conservation planning) is a crucial task. In this sense, conservationplanning is a spatially explicit task that involves many spatially-related decisions, such as establishing the extent of the planningarea (e.g., continent, or river catchment), the size and shape of theunits of planning or the grain size of the biodiversity data used inthe planning process (Mills et al., 2010). However, these decisionsconcerning spatial scale can have important implications for theoutputs of conservation planning assessments and hence the effi-cient use of limited resources for conservation management.

The spatial extent of conservation planning assessments hasvaried from global (Brooks et al., 2004), continental (Carwardineet al., 2008) or finer regional scales such as individual river basins(Hermoso et al., 2011a), islands (Payet et al., 2010) or political

boundaries (Amis et al., 2009). The spatial extent of the planningexercise influences not only the spatial allocation of priority areas,but also the efficiency of the conservation plan and feasibilityof its implementation (Wiersma, 2007; Vazquez et al., 2008).Broad-scale assessments outperform small-scale ones in terms ofefficiency. For example, Kark et al. (2009) found that the same con-servation targets could be achieved at half the cost if a fully coor-dinated (multi-country) conservation plan was carried out acrossthe Mediterranean basin. However, such broad-scale plans oftenrequire coordinated efforts among different regional governmentsand countries which can hinder its application. Planning unit shapevaries according to the realm in which the conservation planningprocess is carried out (e.g., regular grid cells or hexagons interrestrial and marine planning, versus irregular polygons such assub-catchments in freshwater planning). In contrast, the choiceof planning unit size is often arbitrary (Warman et al., 2004),although planning units tend to be larger in assessments of exten-sive regions and smaller in more localized studies (Pressey andLogan, 1998). Determining the appropriate shape and size of plan-ning units is an important consideration in conservation planningbecause it also influences the spatial distribution of priority areas

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⇑ Corresponding author. Tel.: +61 07 3735 5291; fax: +61 07 3735 7615.E-mail address: virgilio.hermoso@gmail.com (V. Hermoso).

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and their efficiency. Previous studies have reported a decrease inefficiency (defined as the ratio of the total area occupied by eachspecies to the total conservation priority area) when using largeplanning units. This is mainly due to the lack of fit between thespecies area of occupancy and the boundaries of planning units,which can result in the selection of areas where the species ofinterest do not occur (Pressey and Logan, 1995, 1998; Presseyet al., 1999; Rouget, 2003). The use of different planning unit sizeshas also proven to affect the identification of conservation priorityareas (i.e. priority maps change with different planning unit sizes)(Rouget, 2003; Warman et al., 2004; Shriner et al., 2006).

Despite the comprehensive scientific literature devoted to eval-uating the effect of the spatial extent of the planning area and plan-ning unit size and shape on conservation outputs, other importantspatial issues have not been adequately assessed. For example,little is known about the effect of the grain size used to representthe spatial distribution of biodiversity surrogates (e.g., species orenvironmental classes). This is a different issue to planning unitsize, since different grain sizes of species distribution data can beused on the same set of planning units (for example, see Fig. 1a).Although fine-grained data on biodiversity surrogates are desirabledue to their greater information content and hence precision of

outcomes (Pressey and Bedward, 1991; Rouget, 2003; Banks andSkilleter, 2007), conservation assessments are usually undertakenat coarse resolution due to limited data quality and quantity. Sothe use of coarse grain data is normally a consequence of dataavailability rather than a conservation planner or stakeholders’decision.

Given that data gathering is an expensive and time-consumingtask, conservation practitioners are usually forced to use the lim-ited available data. These data sources often offer a biased pictureof biodiversity patterns towards well studied areas (e.g., data re-cords from birdwatchers) and are prone to commission or omissionerrors (Loiselle et al., 2003; Rondinini et al., 2006; Hermoso et al.,2011b). Commission or type I errors arise when species are errone-ously expected to be present, which leads to false representation asthe areas selected for conservation do not contain the expectedspecies. Omission or type II errors occur when species are not de-tected, which may result in the selection of areas which are unnec-essarily large or more expensive to manage than needed (Rondininiet al., 2006; Wilson et al., 2005). Coarse representations of speciesdistributions tend to overestimate the species’ area of occupancy,and are therefore prone to commission errors (Rondinini et al.,2006). This is especially the case for rare or patchily distributedspecies (e.g., in highly fragmented areas due to human perturba-tions). Coarse grain sizes also convey an increase in uncertaintyin the spatial distribution of species. For example, a species couldbe expected to occur throughout a large patch if using a coarsegrain size to represent its distribution, but only occupy a smallportion of the patch. These issues have clear implications for theeffective implementation of management actions to conservebiodiversity. Uncertainty concerning the precise distribution ofspecies also makes it difficult to decide where to implement con-servation actions or potentially very expensive if entire expectedareas of occupancy (whole patch) had to be managed. Importantly,these two issues that arise from coarse conservation assessmentsand that could hinder the achievement of efficient conservationoutcomes have not yet been explored.

Here we evaluate the effect of grain size on the efficiency,representativeness (adequate representation of all species), com-mission errors and conservation uncertainty (derived from speciesdistribution uncertainty) of priority areas identified using a sys-tematic conservation planning approach. We focus our study onfreshwater fish biodiversity in the Daly River basin, northernAustralia, an area with important ecological, cultural and economicvalues (Chan et al., 2010). We evaluate the role of two differentsources of grain size variation: (i) grain size of planning units usedfor conservation prioritisation and (ii) grain size used to representthe spatial distribution of species. We use the outcomes of ourstudy to provide critical guidance to conservation researchersand practitioners on the influence of grain size on conservationassessments.

2. Methods

2.1. Study area and species distribution data

The Daly River basin (Fig. 1b) encompasses 53000 km2 and is inrelatively good environmental condition compared to other majorrivers in Australia. The dominant land-uses are low density cattlegrazing and conservation areas (including a few major nationalparks), although small parts of the catchment have been clearedfor more intensive land-uses such as urbanization, pasture andagriculture. The Daly River is currently unregulated, with only asmall volume of groundwater extracted annually for agriculture,but there is considerable pressure for further agricultural develop-ment and water demand (Chan et al., 2010).

Fig. 1. Conceptual representation of different combinations of grain sizes used forplanning units and species distribution data (b) for a sub-catchment (highlighted)of the Daly River basin within northern Australia (a). The shaded area represents thehypothetical distribution of a particular species. The finest grain size of speciesdistribution data (left maps) was used to scale up predictions to coarser grain sizes(right maps). In this way, whenever a species was present in a finer scale planningunit contained within a coarser planning unit, that species was assumed to occupythe entire extent of the coarser planning unit.

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Fish species distribution data was sourced from Kennard(2010). This database contained continuous predictions of the spa-tial distribution for 104 species of freshwater fish across northernAustralia derived from Multivariate Adaptive Regression Splinesmodels (Leathwick et al., 2005) at a fine scale (average area ofpredictive polygons was 3.6 km2). The predictive model was builton a data set of 1609 presence-only records and validated usingan independent data set of 719 presence–absence records (seeHermoso et al. (in press) for more details on predictive models).Predictions for each of the 46 freshwater fish species occurring in14587 predictive units for the Daly River basin were extractedfrom this data base and used for subsequent analyses.

2.2. Identification of priority areas

Priority areas for conservation of freshwater fish in the DalyRiver basin were identified using the software Marxan (Ball et al.,2009). Marxan uses a simulated annealing optimization algorithmto find an optimal combination of areas to represent each speciesat a given target level of occurrence (e.g., 100 km2 of river catch-ment, hereafter termed conservation target). By using the principleof complementarity (gain in representation of biodiversity when asite is added to an existing set of planning units) Marxan tries tofind an optimum set of planning units that achieve the target rep-resentation for each species at the minimum cost. This is done bytrying to minimize an objective function (Formula (1)) that in-cludes the cost of planning units in the solution and other penaltiesfor not achieving the conservation target for all the species(Feature Penalty, weighted by Species’ Penalty Factor, SPF). Weused a high SPF across all analyses (SPF = 100) to ensure all the spe-cies achieved the target. Additional penalties and costs can bespecified in the objective function to force the spatial aggregationof planning units included in the solution and ensure high internalconnectivity within priority areas, and to favor the selection of pri-ority areas that are potentially cheapest to manage. Given our spe-cial interest in exploring the effect of grain size and the lack ofaccurate estimates of conservation cost for the Daly River, we useda constant cost across the study area (all the planning units wereassigned a cost of 1) and we did not include the connectivity termin the objective function. If heterogeneous cost data and connectiv-ity had been used, the Marxan selection processes would havebeen forced to avoid expensive areas and select connected plan-ning units (see Hermoso et al., 2011a), and it would have been dif-ficult to distinguish between the effect of local patterns in cost andconnectivity from the effect of grain size. We made these simplifi-cations to explore the effect of the different grain sizes although weacknowledge that better estimates of conservation or managementcost and the incorporation of connectivity issues would be desir-able for the actual design and implementation of a conservationplan for a particular area.

Objective function ¼X

planning units

Cost

þ SPFX

features

Feature Penalty

þ CSMX

Connectivity Penalty ð1Þ

The optimization algorithm in Marxan was run 100 times (1 M iter-ations each run) to find 100 near-optimal solutions, the best ofwhich was used in the analyses below (called best solutionhereafter).

2.3. Influence of grain size on conservation outputs

Five different scenarios were designed to evaluate the relativeimportance of variation in the grain size of planning units used

for conservation prioritisation and the grain size used to representthe spatial distribution of species (Fig. 1a, Appendix A). These con-servation scenarios were: (i) constant fine planning unit size, withvarying grain sizes of species distributions, (ii) constant coarseplanning unit size, with varying grain sizes of species distributions,(iii) constant fine species distribution grain size, with varying grainsizes of planning units, (iv) constant coarse species distributiongrain size, with varying grain sizes of planning units, and (v) amixed scenario. We varied both planning unit size and grain sizeof species distributions at the same time for the mixed scenario(finest grain size would include both the smallest set of planningunits and finest grain size species distribution, while coarsest op-tion would include the largest set of planning units and coarsergrain size distribution data). Separate analyses were run for thetwo most common types of species distribution data used in con-servation planning: species’ occurrence and area of occupancywithin each planning unit.

2.3.1. Planning unitsRather than using equal-sized grid cells as planning units as is

usually done in terrestrial and marine conservation assessments,we used sub-catchments as they are more appropriate and ecologi-cally relevant for freshwater ecosystems. Moreover, they producemore cost-effective solutions than other regular-shaped grids (Bassetand Edwards, 2003). We used ARC Hydro (Maidment, 2002) forArcGIS 9.3 (ESRI, 2002) to delineate planning units from a 9 seconddigital elevation model. Planning units were separately delineatedfor eleven grain sizes (finest grain size and 10 coarser test grain sizes)(Table 1). Each planning unit included the portion of river length be-tween two consecutive nodes or river connections and its contribut-ing area (ranging from 4 to 1121 km2 on average for the finest andcoarsest grain sizes, respectively). Planning units were spatiallynested across different grain size scenarios, so a coarse planning unitcontained a number of finer size planning units (Fig. 1).

2.3.2. Species distributionsWe translated the predicted spatial distribution of each species

into the finest grain set of planning units. A species was assumed tobe present in a planning unit (and occupy its entire area) wheneverit appeared in at least one of the predictive polygons described ear-lier. This finest grain size of species distribution data was used toscale up predictions to the ten coarser planning unit grain sizes.In this way, whenever a species was present in a finer scale plan-ning unit contained within a coarser planning unit, that specieswas assumed to occupy the entire extent of the coarser planningunit. This approach had been used elsewhere to re-scale predic-tions in conservation planning studies (e.g., Conroy and Noon,1996; Justus et al., 2007). We then assumed each of them to bethe best available representation of species distributions for eachscenario (i.e. as if no better information was available).

The use of coarse grained data to represent species distributionsnecessarily results in higher uncertainty in their actual spatial dis-tribution, especially for rare species or highly fragmented popula-tions. For example, the spatial extent of patchily distributedspecies will be overestimated using coarse grain sizes and will beexpected to occur in areas where they are not present or areas withlow habitat suitability. At each grain size the uncertainty of speciesdistributions (hereafter termed distribution uncertainty) wasquantified for both species’ occurrence and area of occupancy dataas the proportion of expected occurrence or area of occupancy thatwas actually true at the finest grain size averaged across species(Formula (2); Fig. 2a).

Du ¼ 1� 1n

Xn

i¼1

ai

!ð2Þ

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where Du stands for distribution uncertainty, n represents the totalnumber of species and ai the ratio between the areas that each spe-cies truly occupy (finest grain size) and the expected occupied areaat each coarser grain size for each species i.

2.3.3. Conservation targetsA constant target of species representation was used across all

species (i.e. the same number of species’ occurrences or area ofoccupancy). Given that the number of occurrences changed withgrain size (all occurrences within a coarser planning unit becameone) the target was adjusted accordingly to the new grain size torepresent approximately 6% of total number of planning units(Table 1). This was not an issue for species distribution data ex-pressed as area of occupancy, so a constant value was used in thiscase (Table 1).

Similar to Rouget (2003), we compared the results from the dif-ferent conservation planning scenarios against the finest planningunit and species distribution grain size (representing the best po-tential result achievable or ‘‘true situation’’). All best solutions fromthe conservation prioritization analyses run at different grain sizeswere translated into the finest grain size of planning units, so theycould be compared. For example, whenever a coarse planning unitwas included in a best solution, all the finest ones contained withinit were marked as included in the coarse best solution.

We used four different performance measures to evaluate theeffect of grain size of planning units and grain size of speciesdistribution data on conservation outputs.

2.3.3.1. Efficiency. For species’ presence–absence data we measuredthe efficiency of best solutions for each species as the ratio of thenumber of planning units where each species was present withinthe best solution to the total number of planning units in the bestsolution. For species’ area of occupancy data we measured effi-ciency as the ratio of the total area occupied by each species tothe total area of the best solution. The total area or number ofplanning units occupied within the best solution is referred as rep-resentation hereafter.

2.3.3.2. Commission error rate. Commission errors for the range ofgrain sizes of planning units and species distribution data were cal-culated by comparison to the true situation. This was estimated asthe proportion of the expected species’ representation at a givenscale that would not be actually achieved due to overestimationof species occurrences (Formula (3), Fig. 2b).

Ce ¼ 1� rc � rt

rc

� �ð3Þ

where Ce stands for commission errors, rt is the expected true rep-resentation (finest possible grain size) and rc is the actual represen-tation achieved at a given grain size.

2.3.3.3. Conservation uncertainty. Both commission errors and theincrease in uncertainty of species distributions when planning atcoarse grain sizes may have negative consequences for the practi-cal conservation of species. We integrated loss in efficiency anddistribution uncertainty into a single conservation uncertainty in-dex (Formula (3)) to evaluate the influence of different grain sizeson conservation outputs. This index ranges from 0 to 1, 0 indicatingsolutions with the lowest overall uncertainty (low commission er-rors and low distribution uncertainty) and 1 indicating high uncer-tainty in the spatial allocation of species within priority areas andhigh commission errors.

Cu ¼ ð1� CeÞ � Du ð4Þ

where Cu stands for conservation uncertainty, Ce is commission er-ror and Du is the average distribution uncertainty across species fora given grain size (Formula (4); Fig. 2).

Table 1Average area and total number of planning units (n) for each grain size tested in thisstudy. Also shown are the targets for each species distribution data type (occurrenceor area of occupancy) used in the different conservation scenarios. Targets were set tomake the total number or area of planning units comparable across the different grainsizes.

Planning unit Occurrence Occurrence Area(km2)

Grain size(km2)

n Constant planningunit

All otherscenarios

Allscenarios

4.0 13,992 838 838 33637.7 7412 838 436 3363

14.7 3881 838 228 336328.2 2032 838 119 336366.1 865 838 51 3363

130.9 437 838 26 3363197.9 289 838 17 3363273.6 209 838 12 3363430.0 133 838 8 3363602.0 95 838 6 3363

1121.3 51 838 3 3363

Fig. 2. Example calculations for two performance measures on an intermediate grain size. (a) Uncertainty in species distribution was assessed as an average across all speciesusing Formula (2). In this example we show how uncertainty in distribution for each species (ai in Formula (2)) was assessed. The species occupies 0.22, 0.40 and 0.25 ofplanning units 1, 2 and 3 respectively, so the uncertainty in its distribution for this grain size is 0.71 (i.e. 1–0.29). (b) Commission errors were assessed as the proportion of theexpected species’ representation not achieved using Formula (3). In this example, the species was expected to occupy all of planning unit 1 but not occur in the other planningunit that comprised the best solution (note that the additional planning unit might have been necessary to represent other species), but it only occurred in a small proportionof the total area (0.22), so we can infer a commission error of 0.88 (i.e. 1–0.22).

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2.3.3.4. Proportion of species that achieved the conservation target. Wemeasured the proportion of species that achieved the conservationtarget level for each best solution obtained at any given grain sizewhen translated to the finest or true scenario. Although Marxanmight achieve the conservation target for any given grain size, theproportion of species that actually achieved the target at the finestgrain size available might be inflated due to commission errors. So,a species was considered to achieve the target at the finest grain sizeeither when representation in best solution > target level used orrepresentation = maximum area of occupancy predicted at finestgrain size. Given that a common target level was used across allthe species, this could exceed the actual area of occupancy for somerare species. Therefore, a species was also considered to achieve thetarget whenever its entire predicted area of occupancy was includedin the best solution.

We used Generalized Linear Models (GLMs) analyses to testfor significant effects of grain size (of planning units or speciesdistribution data, depending on the conservation scenario) on thevariation of performance measures for each of the five conservationscenarios. We ran independent analyses for each performance mea-sure (dependent variable), with grain size and species identity asexplanatory factors. A gamma distribution of errors with an identitylink function was used across all the GLM models. We also exploredif the effect of grain size was similar across species (all species areequally affected), using a ‘‘homogeneity of slopes’’ analysis. Thehomogeneity of slopes is tested by introducing the interaction term(species x grain size) in each GLM model. Lack of significance of thisinteraction term indicates no significant differences in the slope ofthe relationship between the dependent and the continuous predic-tor (grain size) across factor levels (species).

3. Results

The effect of increasing grain size on the uncertainty in speciesdistributions (averaged across all species) increased in a curvilin-ear fashion up to a maximum of 80.2% and 83.1% for occurrenceand abundance data, respectively, at the coarsest grain size(Appendix B). This increase was stronger for rare than commonspecies (e.g., 96.1%, 79.7% and 2.1% for Neoarius graeffei, Toxoteschatareus and Leiopotherapon unicolor, respectively) due to thehigher expected frequency of occurrence or area of occupancy ofrare species with increasing grain size compared with commonspecies (Fig. 3).

There was little difference between species’ occurrence and areaof occupancy data in the effect of grain size on all performance mea-sures (Table 2). For this reason and the sake of simplicity we referhereafter to results based on area of occupancy data only. Wheneverfine grained planning units or species distribution data were used toidentify conservation priority areas, efficiency in representing spe-cies was usually higher compared to coarse grained data, but it de-clined around 27% on average from the finest to the coarsest grainsizes of the other parameter (Table 2, Fig. 4a). In contrast, use ofcoarse grained data for one parameter resulted in insensitivity tovariation in the grain size of the other parameter (for example effi-ciency for constant coarse planning units was 0.15 and did not varyappreciably with increasing species distribution grain sizes, Fig. 4a).

Commission errors increased markedly with increasing grainsize for all scenarios (up to 93% for the ‘‘constant fine distribution’’scenario), except for the ‘‘constant coarse distribution’’ scenariowhich always had high commission errors (Table 2, Fig. 4b). Theseresults indicate that there was up to 93% of the expected species’representation that would not actually be achieved. These resultsalso indicate that when using coarse grained data for one parame-ter (i.e. planning units or species distributions), the probability ofcommission errors increases, irrespective of the grain size

of the other parameter. Overall conservation uncertainty (whichcombines both commission error and distribution uncertainty) fol-lowed a similar pattern to commission errors for all the scenarios,increasing by up to 97% for the ‘‘Constant fine distribution’’ sce-nario, if the coarsest planning unit grain size had been used insteadof the finest one (Table 2, Fig. 4c).

Commission errors arising from the use of progressively coarserscale species distribution data resulted in progressively fewer spe-cies achieving the representation target when analysed at the finestscale for the ‘‘constant planning unit’’ scenarios (both fine andcoarse) and the ‘‘mixed’’ scenario (Fig. 4d). The decline in the propor-tion of species that achieved the target was up to 70% (i.e. only 30% ofspecies achieved the target when using the coarsest grain size). Thisindicates that independently of the planning unit grain size used,the proportion of species that achieve the target is strongly influ-enced by the coarseness of the species distribution data used. Thisresult was confirmed by the ‘‘constant fine and coarse distribution’’scenarios (Fig. 4d), where the proportion of species that achieved thetarget remained almost invariant across different planning unitgrain sizes, but with very different results. The proportion of speciesthat achieved the target was very high (close to 100%) when fine dis-tribution data was used, while it remained very low (<25%) whencoarse distribution data was used (Fig. 4d).

These results were supported by GLM analyses, where grain sizehad significant effects on efficiency for all scenarios except for thecoarse scenarios (‘‘constant coarse planning unit’’ and ‘‘constantcoarse distribution’’; Appendix C). Grain size had significant effectson commission errors for all scenarios except for the ‘‘constantcoarse distribution’’ scenario and had significant effects on conser-vation uncertainty across all tested scenarios (Appendix C). Theanalysis of homogeneity of slopes showed the interaction term tobe always significant, demonstrating species-specific responses tochanges in grain size for all performance measures (Appendix C).These changes tended to be stronger for rare species than for com-mon ones (Fig. 5).

4. Discussion

Conservation assessments are often carried out at broad spatialextents and rely on coarse resolution data due to constraints on

Fig. 3. Change in expected area of occupancy for three different species in the DalyRiver at three different grain sizes. The spatial distribution of each species at eachgrain size was obtained by scaling up predictions made at the finest scale andattributing this data to the different planning unit grain sizes.

56 V. Hermoso, M.J. Kennard / Biological Conservation 147 (2012) 52–59

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data quality or budgetary and time limitations (Groves et al., 2002).Although some studies indicate that coarse grained biodiversitysurrogates are suitable for conservation planning in homogeneousand relatively pristine areas (Rouget, 2003), there is a general

agreement that finer-scale data should ideally be used where avail-able (Pressey and Logan, 1995, 1998; Rodrigues and Gaston, 2001;Banks and Skilleter, 2007). Previous literature has focused on theevaluation of the effect of the size of planning units on the effi-ciency of priority areas to represent biodiversity (Pressey andLogan, 1994, 1995; Rouget, 2003). However, little is known aboutother spatial related issues, such as the effect of varying resolutionspecies distribution data, or the implications of these factors onother measures of the performance of conservation outputs apartfrom the well-studied effects on efficiency (Wiersma, 2007;Vazquez et al., 2008). Here, we show that the influence of thechoice of grain size of both planning units and species distributiondata extends beyond assessment of efficiency and that the achieve-ment of conservation goals could be seriously compromised bycommission errors and uncertainty in the distribution of speciesassociated with coarse grained assessments. We demonstrate thisby using species distribution data rather than coarse biodiversitysurrogates based on land types, vegetation classes or other envi-ronmental classifications as previously done (Pressey and Logan,1994). To our knowledge, this is the first time that these issueshave been addressed for freshwater ecosystems.

Previous studies have reported a decrease in efficiency withincreasing planning unit size (e.g., Pressey and Logan, 1994,1998; Pressey et al., 1999). This decline has been primarily relatedto the lack of fit between the boundaries of planning units and thespatial extent of species’ area of occupancy (Pressey and Logan,1995). Large planning units will usually contain areas where thespecies are not present, so their distributions patterns could bebetter represented using finer-scale planning units and hence leadto greater efficiency in selection of priority areas. An additionaldrawback associated with large planning units is that they mightbe too big as to be efficiently managed. Other factors such as thenestedness of species distributions have also been cited as animportant factor explaining this reduction in efficiency when usingcomparatively large planning units (Pressey and Logan, 1995).Nhancale and Smith (2011) showed the effect of planning unit sizeon efficiency decreased when cost and connectivity constrainswere applied. Here we argue that the resolution of species distribu-tions should also be considered as an additional factor with strongimplications for conservation assessments. Our study clearly dem-onstrated that the effect of grain size of species distribution datawas more important than planning unit size on conservation plan-ning performance. We found that different planning unit sizes hadno significant effect on efficiency, and commission error rateswhen coarse species distribution data is used (‘‘constant coarsedistribution’’ scenario). Moreover, we found significant changesin commission errors when the size of planning units was keptconstant and the grain size of species distribution varied (up to93% commission errors when using the coarsest species distribu-tion data). In addition, the proportion of species achieving the rep-resentation target was highly dependent on the grain size of thespecies distribution data but insensitive to planning unit size.The effect of cost can be ruled out from these results given thatwe found similar patterns when a spatially heterogeneous surro-gate of cost was used (planning unit area, data not shown).

Table 2Change in performance measures between the finest and coarsest grain sizes tested in each conservation scenario (%Change = (value finest grain size–value coarsest grain size)/value coarsest grain size) � 100). Average values across all species are shown.

Scenario Efficiency Commission errors Conservation uncertainty

Occurrence (%) Area (%) Occurrence (%) Area (%) Occurrence (%) Area (%)

Constant fine planning units �27.0 �26.2 81.1 81.7 91.3 90.9Constant coarse planning units 3.3 1.2 81.9 80.9 90.9 90.8Constant fine distribution �27.5 �27.6 81.9 93.2 90.9 96.5Constant coarse distribution 3.3 5.4 0.6 1.1 9.4 7.6Mixed �27.5 �27.0 81.9 81.0 90.9 91.3

Fig. 4. Effect of grain size on performance measures for the five differentconservation scenarios. See Section 2 for more details on how each performancemeasure was assessed. Trends in average values across all species are shown.

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We also calculated for the first time an overall performance mea-sure of conservation uncertainty by integrating commission errorsand uncertainty in the actual locations where species occur. Thisprovides important information on the effects of grain size whenplanning for conservation of rare species with restricted distribu-tion ranges (Andelman and Willig, 2002) or planning in disturbedlandscapes where biodiversity is distributed in fragmented patches(Rouget, 2003). In these cases where there is a mismatch betweencoarse representations of biodiversity patterns and the smaller

areas of actual occupancy of species, species’ occurrences are over-estimated leading to the inflated commission errors and low repre-sentativeness that we report here. Moreover, coarse resolutionmaps entail high uncertainty on species’ spatial occurrences. Thismakes the planning and implementation of management actionsfor conservation, which are usually required when planning in per-turbed landscapes, more difficult and potentially inefficient. Thelimited budgets available for conservation constrain the total areathat can be effectively managed (e.g., implementation of rehabilita-tion programs). This entails a high risk of misallocating the manage-ment actions in areas where the targeted species do not actuallyoccur; thereby missing the expected benefits from those manage-ment actions (Carwardine et al., 2008). We demonstrate thatthis happens even when using irregular-shaped planning units(sub-catchments) specifically derived to accommodate constraintson the distribution of freshwater biodiversity. Regular-shapedplanning units (e.g., square or hexagonal grids) are normally usedin conservation planning in the terrestrial or marine realms. Weexpect that the use of regular-shaped planning units that are notdefined using ecologically sound criteria would increase the poten-tial for mismatches between species’ actual occurrences and theirrepresentation in planning units.

Despite the benefits of using fine resolution data in conservationplanning, it is not a common practice due to cost constraints. Datagathering to refine species’ distributions can be expensive andtime-consuming, and in many cases may not be possible (Reganet al., 2009). Acknowledging this limitation, some new advancesin predictive modelling and research on different species mappingmethods might help fulfil the necessity of fine resolution speciesdistribution maps. Bombi et al. (2011) showed the pros and consof different methods used to obtain distribution maps for conserva-tion assessment. Stockwell and Peterson (2003) compared differentmethods of mapping biodiversity for gaining spatial resolutionwhen limited data are available. They found species distributionpredictive models produced more reliable and fine resolution datathan the aggregation of species occurrences records or the use ofvegetation surrogates. Araújo et al. (2005) and Barbosa et al.(2009) downscaled coarse distribution maps for different speciesin Europe by using predictive models fitted at coarse grain size tomake predictions at finer resolutions, reporting good predictive per-formances. However, these methods must be used cautiously giventhat additional research is needed to better understand the poten-tial errors in downscaled predictions (Wiens and Bachelet, 2010).

Uncertainty in conservation planning has been associated inpart with inaccuracies in data used for the identification of priorityareas (Regan et al., 2009). Key issues include uncertainties due tothe grain size of planning units as well as the grain size of speciesdistribution data used in conservation planning assessments. Oursensitivity analyses demonstrated the implications of choices con-cerning grain size on the efficiency, commission errors, overall con-servation uncertainty and representativeness of priority areas forconservation. We therefore recommend that: (1) conservationplanners should utilize the finest species distribution data avail-able, particularly when planning for rare species or in fragmentedlandscapes where the limitations of coarse grain sizes are likely tobe greatest; (2) whenever fine grained species distribution data areavailable, the use of coarse planning units should be avoided, (3) asensitivity analyses such as demonstrated here should be under-taken to confirm that the effects of choices of grain size on conser-vation performance (as shown here for freshwater fish) aretransferable to other taxonomic groups and/or realms.

Acknowledgements

We thank J. Stein and D. Ward for their help with the environ-mental characterization of planning units, B. Pusey, D. Burrows, P.

Fig. 5. Effect of grain size on performance measures for the three different species(shown in Fig. 3) under the ‘‘mixed’’ scenario. See Section 2 for more details on howeach performance measure was quantified.

58 V. Hermoso, M.J. Kennard / Biological Conservation 147 (2012) 52–59

Author's personal copy

Bayliss and J. Boyden for assistance with compilation of fish andwaterbird data and S. Linke for discussions in the initial planningstages of this paper. We acknowledge the Australian GovernmentDepartment of Sustainability, Environment, Water, Populationand Communities, the National Water Commission, the TropicalRivers and Coastal Knowledge (TRaCK) Research Hub, and the Aus-tralian Rivers Institute, Griffith University, for funding this study.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.biocon.2012.01.020.

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