predicting species distributions: use of climatic

Ecological Modelling 186 (2005) 250–269

Predicting species distributions: use of climatic parameters inBIOCLIM and its impact on predictions of species’ current and

future distributions

Linda J. Beaumonta, ∗, Lesley Hughesa, Michael Poulsenb

a Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australiab Department of Human Geography, Macquarie University, NSW 2109, Australia

Received 9 May 2004; received in revised form 11 January 2005; accepted 17 January 2005Available online 17 February 2005

Abstract

Bioclimatic models are widely used tools for assessing potential responses of species to climate change. One commonly usedmodel is BIOCLIM, which summarises up to 35 climatic parameters throughout a species’ known range, and assesses the climaticsuitability of habitat under current and future climate scenarios. A criticism of BIOCLIM is that the use of all 35 parametersmay lead to over-fitting of the model, which in turn may result in misrepresentations of species’ potential ranges and to theloss of biological reality. In this study, we investigated how different methods of combining climatic parameters in BIOCLIMinfluenced predictions of the current distributions of 25 Australian butterflies species. Distributions were modeled using threep tion of them the valuesf fluence thed ssessed thee ate changes arametersi ributions.T rage half thes uenced, toa ical zonesm the sames re predictedt of predictedd nces in thep ecrease bya espectively.

0d

reviously used methods of selecting climatic parameters: (i) the full set of 35 parameters, (ii) a customised selecost relevant parameters for individual species based on analysing histograms produced by BIOCLIM, which show

or each parameter at all of the focal species known locations, and (iii) a subset of 8 parameters that may generally inistributions of butterflies. We also modeled distributions based on random selections of parameters. Further, we axtent to which parameter choice influenced predictions of the magnitude and direction of range changes under two climcenarios for 2020. We found that the size of predicted distributions was negatively correlated with the number of pncorporated in the model, with progressive addition of parameters resulting in progressively narrower potential disthere was also redundancy amongst some parameters; distributions produced using all 35 parameters were on aveize of distributions produced using only 6 parameters. The selection of parameters via histogram analysis was infln extent, by the number of location records for the focal species. Further, species inhabiting different biogeographay have different sets of climatic parameters limiting their distributions; hence, the appropriateness of applying

ubset of parameters to all species may be reduced under these situations. Under future climates, most species weo suffer range reductions regardless of the scenario used and the method of parameter selection. Although the sizeistributions varied considerably depending on the method of selecting parameters, there were no significant differeroportionalchange in range size between the three methods: under the worst-case scenario, species’ distributions dn average of 12.6, 11.4, and 15.7%, using all parameters, the ‘customised set’, and the ‘general set’ of parameters, r

∗ Corresponding author. Tel.: +61 2 9850 8191; fax: +61 2 9850 8245.E-mail address:[email protected] (L.J. Beaumont).

304-3800/$ – see front matter © 2005 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2005.01.030

L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 251

However, depending on which method of selecting parameters was used, the direction of change was reversed for two speciesunder the worst-case climate change scenario, and for six species under the best-case scenario (out of a total of 25 species).These results suggest that when averaged over multiple species, the proportional loss or gain of climatically suitable habitat isrelatively insensitive to the number of parameters used to predict distributions with BIOCLIM. However, when measuring theresponse of specific species or the actual size of distributions, the number of parameters is likely to be critical.© 2005 Elsevier B.V. All rights reserved.

Keywords:BIOCLIM; Bioclimatic envelope; Butterflies; Climate change; Predictive modeling; Range shifts

1. Introduction

Over the past century, global average surface tem-perature has increased approximately 0.6◦C (IPCC,2001). There is a growing body of literature revealingconsistent responses of plants and animals to the tem-perature increase experienced so far (Parmesan et al.,1999; Pounds et al., 1999; Thomas and Lennon, 1999;Hughes, 2000; Kiesecker et al., 2001; McCarthy, 2001;Thomas et al., 2001; McLaughlin et al., 2002; Waltheret al., 2002; Forister and Shapiro, 2003; Hughes, 2003;Parmesan and Yohe, 2003; Root et al., 2003; Stefanescuet al., 2003). In a meta-analysis of more than 1700species,Parmesan and Yohe (2003)found that recentbiological trends such as range shifts and advancementof spring events are consistent with predictions of re-sponses to global warming; they conclude that there is avery high level of confidence that global warming hasalready affected organisms. The IPCC has predicted

cultivation (Jovanovic et al., 2000; Cunningham et al.,2002). Importantly, species distribution models are cur-rently the only means by which we can assess the poten-tial magnitude of changes in the distributions of multi-ple species in response to climate change (e.g.Breretonet al., 1995; Eeley et al., 1999; Beaumont and Hughes,2002; Berry et al., 2002; Erasmus et al., 2002; Midgleyet al., 2002; Peterson et al., 2002; Peterson, 2003;Williams et al., 2003; Meynecke, 2004; Thomas et al.,2004). Recently, distribution models have been used toassess the feasibility of current conservation strategiesand the value of existing reserves in Great Britainunder future climate scenarios (Dockerty et al., 2003;Hossell et al., 2003) and to examine the effects that dif-ferent climate regimes may have on biodiversity withinexisting South African National Parks (Rutherford etal., 1999). The output of these models has also beenused to estimate extinction probabilities of species inresponse to global warming (Thomas et al., 2004).

ofocli-re-

arisegepe’.

that by the end of this century, average temperatureincrease could be as high as 6◦C (IPCC, 2001). Assome species have already responded to a temperatureincrease of 0.6◦C, it is clear that more substantial ef-fects on species and ecosystems will occur in the future(Root et al., 2003).

Predicting the current or future distributionsspecies has principally been conducted using bimatic models that assume that climate ultimatelystricts species distributions. These models summa number of climatic variables within the known ranof a species, thus generating a ‘bioclimatic envelo

To understand the impacts of future climate change,it is imperative that we can confidently predict thec ies.S e ofa tentiat ;S i-o .,2 fep -ee ns or

The models can then be used to (a) identify the speciescurrent potential distribution, that is, all areas with cli-m opea imat-i

ticm andd lsp onso on,2 gl m ac

urrent and future potential distributions of specpecies distribution models have a broad rangpplications, and have been used to assess the po

hreat of pests or invasive species (Ungerer et al., 1999utherst et al., 2000), to obtain insights into the blogy and biogeography of species (Anderson et al002; Steinbauer et al., 2002), to identify hotspots ondangered species (Godown and Peterson, 2000) orredict biodiversity (Maes et al., 2003), to prioritise aras for conservation (Chen and Peterson, 2002), and tostablish suitable locations for species translocatio

l

atic values within the species bioclimatic envelnd (b) assess whether these areas will remain cl

cally suitable under future climate scenarios.While criticisms have been leveled at bioclima

odels due to their exclusion of biotic interactionsispersal scenarios (Davis et al., 1998), these modelay a vital role in assessing potential distributif species (Baker et al., 2000; Pearson and Daws003), and are useful ‘first filters’ for identifyin

ocations and species that may be most at risk frohanging climate (Chilcott et al., 2003). Bioclimatic

252 L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269

models often represent the most feasible method ofexamining potential distributions of species for anumber of reasons. First, the cost of field surveysto assess species distributions can be prohibitive,especially if a large number of species is involved:bioclimatic models can be used to extrapolate habitat-specific information from one region to another toassess the likelihood of the presence of a speciesor multiple species. Second, when little is knownabout the ecology and biology of a species, suchmodels provide the only method of estimating cur-rent and future potential distributions (Baker et al.,2000). Finally, a large number of datasets, such as thosederived from museum and herbaria records, consist ofpresence-only data (Austin, 1994). While these datacannot be easily examined using conventional spatialstatistics, they are ideally suited for some types of bio-climatic models (Burgman and Lindenmayer, 1998;Kadmon et al., 2003). Consequently, bioclimaticmodels are an important and widely used tool forassessing the potential responses of species to climatechange (Guisan and Zimmermann, 2000).

BIOCLIM (developed conceptually byNix, 1986)and related GIS approaches, have been widely usedto generate bioclimatic profiles and to assess thecurrent and future potential distributions of a widerange of taxa in Australia, South Africa, and SouthAmerica (Campbell et al., 1999; Eeley et al., 1999;Jackson and Claridge, 1999; Dingle et al., 2000; Doranand Olsen, 2001; Fischer et al., 2001; Backhouse andB idge,2 002;L ,2 here pt atice thep s inp her,i berso eadt ofs l.,2 no beingc thes ouldo eters

may place unrealistic constraints on identifying climat-ically suitable habitat. Similarly, parameters that may infact limit a species distributions are excluded from themodel, the predicted distributions may have increasedcommission error rates, i.e. the species is predicted tooccur in a given location when in fact it does not (fora discussion of prediction errors seeFielding and Bell,1997). Hence, the number of parameters included in amodel is an important consideration because using toofew, or too many parameters, may result in incorrectpredicted distributions. This could lead to inaccurateidentification of species at risk and, subsequently, tounsound management decisions. Further, the extent towhich errors in over- or under-estimating current po-tential distributions may propagate under models of fu-ture climates is unknown. A clear understanding of therelationship between over-fitting and the magnitude ofpredicted range changes under future climate changescenarios is necessary if predictions from BIOCLIMand other related models are to be useful, credible man-agement tools. Therefore, the aims of this study were:

(1) To investigate how different methods of combiningclimatic parameters within BIOCLIM may influ-ence the predicted distributions of 25 Australianbutterfly species.

(2) To determine the extent to which the selection ofclimatic parameters influenced the magnitude anddirection of predicted changes in range under fu-ture climate change scenarios. Specifically, as con-

obalioniching

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urgess, 2002; Beaumont and Hughes, 2002; Clar002; Cunningham et al., 2002; Steinbauer et al., 2oiselle et al., 2003; Tellez-Valdes and Davila-Aranda003; Williams et al., 2003; Meynecke, 2004; Waltt al., 2004). Although BIOCLIM can interpolate u

o 35 climatic parameters to define a species climnvelope and to predict its potential distribution,rogressive addition of climatic parameters resultrogressively smaller potential distributions. Furt

t has been argued that the inclusion of large numf parameters in models such as BIOCLIM may l

o misrepresentations of the potential distributionpecies (Kriticos and Randall, 2001; Chilcott et a003; Williams et al., 2003). For example, inclusiof unnecessary parameters may result in areaslassified as climatically unsuitable when in factpecies could occur there (omission errors). This cccur because inclusion of unnecessary param

tractions of species ranges in response to glwarming may increase the likelihood of extinct(Thomas et al., 2004), we assess the extent to whpredictions of habitat loss under global warmscenarios may be an artefact of over-fitting.

3) To investigate the role that biogeography mayin the selection of the most appropriate groupparameters.

4) To investigate the extent to which the numbeknown locations of a species can influence thelection of climatic parameters and the size of pdicted distributions.

. Methods

.1. BIOCLIM

BIOCLIM is a correlative modeling tool that inteolates up to 35 climatic parameters (Table 1) for any


Table 1Bioclimatic parameters used in BIOCLIM v5.1

1 Annual mean temperature2 Mean diurnal range3 Isothermality4 Temperature seasonality5 Max temperature of warmest period6 Min temperature of coldest period7 Temperature annual range8 Mean temperature of wettest quarter9 Mean temperature of driest quarter

10 Mean temperature of warmest quarter11 Mean temperature of coldest quarter12 Annual precipitation13 Precipitation of wettest period14 Precipitation of driest period15 Precipitation seasonality16 Precipitation of wettest quarter17 Precipitation of driest quarter18 Precipitation of warmest quarter19 Precipitation of coldest quarter20 Annual mean radiation21 Highest period radiation22 Lowest period radiation23 Radiation seasonality24 Radiation of wettest quarter25 Radiation of driest quarter26 Radiation of warmest quarter27 Radiation of coldest quarter28 Annual mean moisture index29 Highest period moisture index30 Lowest period moisture index31 Moisture index seasonality32 Mean moisture index of high quarter33 Mean moisture index of low quarter34 Mean moisture index of warm quarter35 Mean moisture index of cold quarter

location for which the latitude, longitude, and eleva-tion are known (for a full description of BIOCLIM, seeNix, 1986; Houlder et al., 2001). While primarily usedin the Southern Hemisphere, BIOCLIM can use cli-mate surfaces generated from meteorological data forany country. For Australia, climate surfaces have beengenerated from long-term monthly averages of climatevariables at over 900 temperature stations and 11,000precipitation stations throughout the continent (Busby,1991).

BIOCLIM can be used for three tasks (a) describ-ing the environment in which the species has beenrecorded, (b) identifying other locations where thespecies may currently reside, and (c) identifying where

the species may occur under alternate climate scenar-ios. The program interpolates a species bioclimatic en-velope, which is a summary of the climate at locationsfrom where the species has been recorded. BIOCLIMis a range-based model that describes a species climaticenvelope as a rectilinear volume (Fig. 1), that is, it sug-gests that a species can tolerate locations where valuesof all climatic parameters fit within the extreme valuesdetermined by the set of known locations (Carpenteret al., 1993). The current potential distribution of aspecies is identified by interpolating the climate withineach grid cell of a Digital Elevation Model (DEM) andcomparing it to the climatic profile of the species. Lo-cations with values of all climatic parameters withinthe range of the species profile are classified by BIO-CLIM as climatically suitable. However, multiple lev-els of classification can be achieved by removing theextreme values of each parameter, and identifying loca-tions with climatic values that lie within different per-centile limits. For example, locations where the valuesof all parameters lie within the 5–95th percentiles ofthe species envelope may be classified as ‘core’ regions(Fig. 1).

F two-d eana wn lo-c l dis-t int le. Thed side oft . Thisfi

ig. 1. Diagrammatic representation of a hypotheticalimensional bioclimatic envelope. Dots represent values of mnnual temperature and mean annual precipitation at each knoation of a hypothetical species. In predicting a species’ potentiaribution, BIOCLIM would classify all locations with values withhe extremes of the species envelope (unbroken line) as suitabashed box represents those areas where climatic values out

he 5–95th percentiles of the species envelope are excludedgure has been modified fromCarpenter et al. (1993).


Originally, BIOCLIM incorporated 12 climaticindices, based on temperature and precipitation.Nix(1986) argued, however, that while each of the 12indices provided useful discrimination in particularapplications, the seasonality of temperature andprecipitation was not adequately described. Hence,additional indices were incorporated into later ver-sions of the program, resulting in a more completedescription of the bioclimatic envelope. These includethe coefficient of variation of monthly temperature andof precipitation, a measure of isothermality, and themean temperature and precipitation for the warmestand coldest quarters. Within BIOCLIM (v5.1), the usercan select which of the climatic parameters to includewhen identifying suitable habitat. The disadvantage ofusing less than the full set of parameters is that somepossible interactions and partial substitutions betweenindices may be excluded (Martin, 1996). For example,although an area may have low rainfall, this may becompensated to an extent by lower evaporation, whichin turn will depend upon temperature and radiation(Nix, 1986). Therefore, a moisture index was added tolater versions of BIOCLIM (Martin, 1996).

2.2. Species selection

We selected 25 species of butterflies from five bio-geographic zones in Australia (Montane, NortheastQueensland, East coast, Southeast coast, and thosebroadly distributed across Australia,n= 5 in eachz iquel reo aseo 00r m-p mto2( ayh ca-t fur-t

g aDw oxi-m ccu-r

Table 2Biogeographical zones for Australian butterfly species, for whichpotential distributions were modeled

Species Biogeographical zone

Anisynta dominula MontaneArgynnina cryila Southeast AustraliaCandalides erinus Broadly distributedDelias nysa East coastDispar compacta Southeast AustraliaElodina queenslandica Northeast QueenslandHesperilla donnysa Broadly distributedHeteronympha paradelpha Southeast AustraliaHypocysta metirius East coastJalmenus icilius Broadly distributedMesodina halyzia East coastNeolucia hobartensis MontaneNetrocoryne repanda East coastOcybadistes walkeri Broadly distributedOgyris amaryllis Broadly distributedOreisplanus munionga MontaneOreixenica latialis MontaneOreixenica orichora MontanePantoporia consimilis Northeast QueenslandParalucia aurifer Southeast AustraliaPhiliris nitens Northeast QueenslandPseudalmenus chlorinda Southeast AustraliaTagiades japetus Northeast QueenslandTellervo zoilus Northeast QueenslandTrapezites eliena East coast

in mountainous regions (D. Houlder, personal commu-nication).

2.3. Current potential distributions

We produced current potential distributions for eachof the 25 butterfly species using 4 different methods ofselecting climatic parameters.

2.3.1. Full setPotential distributions were produced using all 35

climatic parameters available in BIOCLIM v5.1. To-gether, these produce the smallest possible distributionfor each species predicted by the program.

2.3.2. Customised setFor each species, we produced a ‘customised’ set

of parameters that we considered most relevant foreach individual species. Selection of these parameterswould ideally be based on knowledge of the biologyof the species in question. However, when the biology

one), for which we had records from at least 50 unocations (Table 2, Fig. 2). Species distributions webtained from the Dunn & Dunn National Databf Australian Butterflies, which contains over 110,0ecords of the collection locations of butterflies, coiled from public and private collections, and fro

he literature (Dunn and Dunn, 1991). Distributionsf all species were mapped in ArcView v3.2 (ESRI,000) and compared to maps inButterflies of AustraliaBraby, 2000) to identify anomalous points that mave resulted from incorrect geocoding or identifi

ion. Questionable locations were removed fromher analysis.

The elevations of all locations were derived usinigital Elevation Model, Aus40.DEM (CRES, 1999),hich has a resolution of 1/40 of a degree (apprately, 2.5 km grid squares). This DEM has an a

acy of±10 m in relatively flat topography and±100 m


Fig. 2. Locations of biogeographical zones from which 25 Australian butterfly species used in this study occur. Broadly distributed species werefound across the continent. East coast species are located along most of the East coast.

of the species is not fully known, as is the case withmany species, these parameters can be selected basedon histograms produced by BIOCLIM, which showthe frequency distribution of values of each climaticparameter throughout the species’ known range. Forexample, it can be hypothesised that parameters withnormally distributed values may be an important in-fluence on the species distribution (Fig. 3a). Similarly,parameters that are highly skewed may also be relevantto the species distribution (Fig. 3b). Parameters withskewed distributions may be those that do not havea negative value, such as rainfall, and those that havevalues between zero and one, such as moisture indices.Where there is no clear pattern in the histograms for aparameter, that parameter could be classified as irrel-evant, i.e. it does not appear to influence the speciesdistribution (Fig. 3c). Similarly, where the histogram isnormally distributed but is truncated in one or both tails,the parameter could also be rejected, as these graphssuggest that the species could tolerate other values ofthis parameter that were not included in the speciesclimatic envelope (Fig. 3d). This may occur if thedistribution records for a species do not cover its entiregeographic range. The usefulness of individual param-eters can also be assessed by comparing the values ofa parameter within the species envelope to the rangeof values of that parameter across the study region.For example, a histogram of the values of precipitation

in the driest quarter throughout the known range ofNetrocoryne repandais normally distributed, with val-ues throughout the species distribution ranging from 0to 267 mm. This species is found along the East coastof Australia, from Cape York to Victoria. However, asthe precipitation of the driest quarter throughout mostof Australia is within the range to which the speciesis currently exposed, it is unlikely that this parameteris limiting the distribution ofN. repanda. Hence, thisparameter was not used to predict this species potentialdistribution.

We visually examined the histograms of each ofthe 35 climatic parameters for each of the 25 species,and subjectively classified the parameter as relevant ornot, thus identifying a ‘customised’ set of parametersrelevant for each species. Potential distributions werederived for each species using their ‘customised’ setof parameters. The number of climatic parametersselected for each species ranged from 3 to 16, with anaverage of 7 (±S.D. 3).

2.3.3. Generalised setAn alternative method of selecting climatic param-

eters in BIOCLIM is to identify a subset of parametersapplicable to the taxon or habitat in question, andapply these to all species (e.g.Williams et al., 2003).We identified which of the 35 parameters we had clas-sified as relevant in the ‘customised’ sets for at least


Fig. 3. Examples of frequency distribution histograms produced by BIOCLIM for values of climatic parameters throughout the network of aspecies’ collection locations. Patterns of histograms were examined to assess whether the parameter may be influencing the species distribution.Histograms that were (a) normally distributed and (b) skewed, were classified as relevant, while histograms with (c) no pattern or (d) truncated,i.e. values of tails missing, were classified as irrelevant.

one-third of our species. Eight parameters fulfilledthis criteria, and this comprised our ‘generalised’set: annual mean temperature, mean diurnal range,max temperature of warmest period, min temperatureof coldest period, temperature annual range, meantemperature of warmest quarter, mean temperatureof coldest quarter, and annual precipitation. Wemodeled potential distributions for each species usingthis set.

We calculated the percent increase in the size ofspecies current potential distributions using (a) the‘customised’ set of parameters and (b) the ‘generalised’set of parameters, compared to distributions for eachspecies based on all 35 parameters. Linear regressionswere used to assess the relationship between the changein size of these distributions and the number of climaticparameters selected.

2.3.4. Random setFor five randomly selected species (Anisynta

dominula, Oreixenica latialis, Hypocysta metirius,N. repanda, Heteronympha paradelpha), we createdpotential distributions using random sets of climaticparameters. We generated sets of parameters com-prising 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25,27, 29, 31, and 33 parameters each. Five of each setof parameters were generated (i.e. a total of 85 setsof parameters). For each of the 5 species, we usedeach of the 85 sets of random parameters to createnew potential distributions. We calculated the meanincrease in the size of potential distributions derivedfrom each set of random parameters for each species,compared to distributions derived using all 35 climaticparameters. The relationship between the meanpercent change in distribution size versus the number


of climatic parameters was assessed using logarithmicregression.

To determine whether different classes of param-eters contributed more to the size of current potentialdistributions than others, we created current potentialdistributions for each of the five randomly selectedspecies using (a) temperature parameters only, (b)precipitation parameters only, (c) radiation parametersonly, (d) moisture index parameters only, (e) all pa-rameters except temperature, (f) all parameters exceptprecipitation, (g) all parameters except radiation,(h) all parameters except moisture indices, and allpossible pair-wise combinations of parameter classes.Again, we calculated the change in the size of speciesdistributions derived from the above sets of parameterscompared to potential distributions derived using all 35parameters.

2.4. Effects of biogeography

The 25 butterfly species used in this study came fromfive different biogeographical zones of Australia (Mon-tane, Northeast Queensland, East coast, Southeastcoast, and those broadly distributed across Australia,n= 5 in each zoneFig. 2). We hypothesised that theapplication of a generalised set of climatic parametersto all species may be inappropriate if the distributionsof species inhabiting different biogeographical zonesare limited by different climatic parameters. Weconducted Similarity Percentages (SIMPER) andA .0( ticp eciesw eo-g entz

2

eciesw pat-t rds isl cies(e lyr een3 atinge the

process of analysing the histograms, selecting relevantparameters, and modeling potential distributions.

2.6. Climate change scenarios

To assess whether the method of selecting climaticparameters influenced the magnitude and/or directionof change of predicted distributions under future cli-mates, we derived two climate change scenarios forthe year 2020 using OzClim v2.0.1 (CSIRO, 1996).These scenarios were developed by the CSIRO At-mospheric Research Unit and the International GlobalChange Institute. The models cover the range of un-certainty associated with future global warming dueto different greenhouse gas emissions and climatesensitivities (K. Hennessy, personal communication).The ‘worst-case’ (i.e. the greatest change from cur-rent climate) scenario used the Global CirculationModel (GCM) CSIRO Mk 2, with the SRES sce-nario A1F and high climate forcing. This model pro-duces a hot/dry response. The ‘best-case’ (the leastchange from current climate) scenario used the DAR-LAM model with the SRES scenario B1 and lowclimate forcing, producing a wet/warm response (K.Hennessy, personal communication). Changes in min-imum temperature, maximum temperature, and precip-itation were extracted for each 1◦ latitude/longitudecell across Australia. To assess how species distribu-tions may change, we created new climatic grids inBIOCLIM that incorporated the changes in temper-a rflys us-i ed’s t ofp

tivet and‘ achs es inq tionsw and9 alueo fectt haveoe

tiono to

nalysis of Similarity (ANOSIM) using Primer v5.2Primer-E, 2001) to determine whether the climaarameters we had selected for each individual spere similar for species within the same biographical zone, and different for species in differones.

.5. Effects of the number of known records

Our results may have been biased against spith fewer distribution records because histogram

erns are not always clear when the number of recoow. Hence, we randomly selected five butterfly speHesperilla donnysa, H. metirius, N. repanda, Oreix-nica orichora, Trapezites eliena), and then randomemoved two-thirds of their records, leaving betw0 and 121 records for each species. After re-creach species’ climatic envelope, we repeated

ture and precipitation. For each of the 25 buttepecies, we produced future potential distributionsng (a) all climatic parameters, (b) the ‘customiset of parameters, and (c) the ‘generalised’ searameters.

The change in the size of future distributions relao current ones was calculated for both the ‘range’core’ regions of each predicted distribution for epecies. All locations predicted to contain the speciuestion are termed ‘range’ areas, while those locahere the values of parameters lie within the 5th5th percentiles are termed ‘core’ regions. The vf studying ‘core’ regions is that it reduces the ef

hat outliers, or non-representative observations,n the sensitivity of predicted distributions (Kadmont al., 2003).

We used ANOVA’s to assess whether the proporf loss or gain of future distributions compared


current distributions was similar among the threemethods of selecting parameters.

3. Results

3.1. Current potential distributions

3.1.1. Full setWhen all 35 climatic parameters were included, the

mean size of the 25 species distributions was 105,187grid cells (±S.D. 185,089), where each grid cell is1/40th of a degree (Fig. 4). Standard deviations werelarge, because some species have restricted distribu-tions while others are distributed widely. For example,O. latialiswas predicted to have the narrowest distribu-tion, with a total of 3039 cells identified as climaticallysuitable, whileOcybadisteswalkerihad the largest pre-dicted distribution, covering 334,223 grid cells (Fig. 5).

3.1.2. ‘Customised’ setThe number of climatic parameters selected from

examination of histograms for each species ranged

Fig. 4. The average size of 25 butterfly species distributions (numberof grid cells where each cell is 1/40th of a degree latitude/longitude),predicted using different numbers of climatic parameters in BIO-CLIM. All = all 35 parameters, general = a subset of 8 parameters,customised = customised sets of parameters used for each species(mean per species = 7± 3.6). Bars represent standard deviations.

from 3 to 16 (N= 25, mean = 7,±S.D. 3). The meannumber of grid cells selected as climatically suitablewas 184,368 (±S.D. 282,101). Distributions were, onaverage, 2.3 times larger than those derived using all pa-rameters (±S.D. 1.2).Tagiades japetuswas predicted

F walkeri, and the predicted current distributions modeled in BIOCLIM using( ers for this species, and (d) a ‘general’ set of parameters applicable to all butterflys ray areas represent ‘range’ regions.

ig. 5. Comparisons of the (a) known distribution ofOcybadistesb) all 35 climatic parameters, (c) a ‘customised’ set of parametpecies. Dark gray areas represent ‘core’ regions, while light g


to have the narrowest distribution, with a total of 13,533cells identified as climatically suitable, whileOgyrisamaryllishad the largest predicted distribution, cover-ing 1,089,072 grid cells.

3.1.3. ‘Generalised’ setWe visually assessed the kurtosis of histograms that

BIOCLIM produced of values for each climatic param-eter throughout each species known range, and identi-fied 8 parameters that commonly appeared to influencethe distributions of the 25 butterfly species. These pa-rameters were annual mean temperature, mean diurnalrange, max temperature of warmest period, min tem-perature of coldest period, temperature annual range,mean temperature of warmest quarter, mean tem-perature of coldest quarter, and annual precipitation.The mean size of the current distributions using this‘generalised’ set of parameters was 153,126 grid cells(±232,901), and they were, on average, 1.7 times largerthan those derived using all parameters (±S.D. 0.3).O.latialis was predicted to have the narrowest distribu-tion, with a total of 5616 cells identified as climaticallysuitable, whileO. amaryllishad the largest predicteddistribution, covering 945,322 grid cells (Fig. 5).

Intuitively, the inclusion of more climatic parame-ters will place tighter constraints on classifying habitatas climatically suitable, and hence the size of potentialdistributions will decrease. The percent change in thesize of current distributions using the ‘customised’ setsof parameters compared to all parameters (dependentv tersf gnif-i ,P thes ed’s et ofp er of‘ dentv ted(

3hip

b tersaP dst erei reby

further addition of parameters has progressively lesseffect on the size of predicted distributions. For ex-ample, on average a distribution predicted from all 35parameters is 40% of the size of a distribution pre-dicted from 5 parameters, 80% of the size of a dis-tribution predicted from 15 parameters, and 89% ofthe size of a distribution predicted from 25 parameters(Fig. 6). Furthermore, an average of 67% of cells iden-tified as containing climatically suitable habitat usingthe ‘customised’ method, were also suitable using thesame number of randomly selected climatic parame-ters (±S.D. 16%). Redundancy of parameters was alsoshown by including or excluding different classes of pa-rameters (Table 3, Fig. 7). For example, the inclusionof all temperature and radiation parameters but no pre-cipitation or moisture indices, resulted in distributionsthat were only 16% larger on average than those us-ing all 35 parameters. Similarly, the exclusion of eitherthe precipitation or the moisture index parameters didnot change the size of the potential distribution sub-stantially (4 and 5% increase, respectively;Table 3).Comparisons of groups of parameters suggest that pre-cipitation and moisture index parameters were not asuseful for defining the suitability of habitat for the 25species, compared to temperature and radiation param-eters (Table 3).

F r fiveb tiono ize ofs . Barsr ropor-t tiond rame-t

ariable), and the number of ‘customised’ parameor each species (independent variable), was sicantly negatively correlated (r2 = 0.38, F= 13.86

= 0.001,n= 25). Similarly, the percent change inize of current distributions using the ‘customisets of parameters compared to the ‘general’ sarameters (dependent variable), and the numb

customised’ parameters for each species (indepenariable) was also significantly negatively correlar2 = 0.46,F= 19.88,P= 0.0001,n= 25).

.1.4. Random setThere was a highly significant negative relations

etween progressive addition of random paramend the size of species distributions (r2 = 0.98,F= 988,< 0.001,n= 16). While addition of parameters lea

o progressively smaller predicted distributions, ths a level of redundancy among parameters, whe

ig. 6. The average agreement in the size of distributions foutterfly species predicted using BIOCLIM by continual addif randomly selected climatic parameters, compared to the species distributions modeled using all 35 climatic parametersepresent standard deviations. Agreement is defined as the pion of cells classified as climatically suitable in both the distribuerived using random parameters and that derived using all pa

ers.


Table 3Average increase in the predicted distributions of 5 butterfly speciesderived from subsets of climatic parameters, compared to distribu-tions derived using 35 climatic parameters

Parameter groups % Increase in distributionsize mean (S.D.)

All but temperature 76 (119)All but precipitation 4 (3)All but radiation 18 (10)All but moisture index 5 (4)Temperature and precipitation 30 (16)Temperature and radiation 16 (9)Temperature and moisture 31 (16)Precipitation and radiation 103 (156)Precipitation and moisture 239 (396)Radiation and moisture 85 (122)Temperature only 131 (140)Precipitation only 447 (739)Radiation only 168 (266)Moisture indices only 380 (619)

3.2. Effects of biogeography

We assessed whether the parameters selected assuitable for species in the same biogeographicalregions of Australia were similar. Generally, thepercent similarity (SIMPER) among butterfly specieswithin a particular zone was low, ranging from 22%

for those species found in Northeastern Australia, to55% for species in the southeast (Table 4). We alsocompared the differences in parameters classified asrelevant for species found in different biogeographicalzones across Australia. Of 10 possible pair-wisecomparisons of 5 biogeographical zones, there weresignificant differences in the selection of parametersfor 4 pairs: Montane and broadly distributed; Eastcoast and broadly distributed; broadly distributedand Southeast coast; Southeast and Northeast coast(Table 4).

3.3. Effects of the number of known records

There was a weak relationship between the numberof climatic parameters selected as relevant after visu-ally examining histograms, and the number of locationrecords for each species (r2 = 0.15,F= 4.11,P= 0.054,n= 25). This reflects the difficulty in interpreting his-togram distribution patterns for species with fewerlocation records. Similarly, after randomly removingtwo-thirds of the location records of five species andre-interpreting their histograms, fewer climatic param-eters were chosen for four of the five species (H. don-nysa, H. metirius, O. latialis, T. eliena), while for thefifth species (N. repanda) the same number of param-eters was chosen.

Fig. 7. Comparisons of current potential distributions predicted by BI (b)temperature and radiation parameters only, and (c) precipitation and gions, whilelight gray areas represent ‘range’ regions.

OCLIM forAnisynta dominulausing (a) all 35 climatic parameters,moisture parameters only. Dark gray areas represent ‘core’ re


Table 4(a) Within group similarity (ANOSIM) in climatic parameters clas-sified as relevant for butterfly species in five biogeographical regionsin Australia, (b) between group dissimilarity (SIMPER) for eachpair-wise combination of biogeographical regions

(a) Within group similarity % Similarity

Montane 34.2East coast 38.6Broadly distributed 38.4Southeast Australia 55.3Northeast Queensland 22.0

(b) Between group dissimilarity % Dissimilarity

Montane and East coast 73.2Montane and broadly distributed* 74.5East coast and broadly distributed* 70.8Montane and Southeast Australia 60.0East coast and Southeast Australia 61.0Broadly distributed and Southeast Australia* 60.8Montane and Northeast Queensland 77.3East coast and Northeast Queensland 76.2Broadly distributed and Northeast Queensland 66.9Southeast Australia and Northeast Queensland* 70.6

∗ P< 0.05.

The climatic envelope is a summary of climatethroughout the species known range. Hence, a climaticenvelope based on a subset of species records may beexpected to have a narrower range of climate valuesthan an envelope based on all of a species’ locationrecords. For the five species in this part of our study,removal of two-thirds of the known locations decreasedthe range of each climatic variable by an average 9.4%(S.D. 9.6%). As a result, potential distributions derivedfrom a subset of location records were 45% smaller onaverage (±S.D. 19%) than when using the full set ofknown locations.

3.4. Climate change scenarios

To assess how the method of selecting climatic pa-rameters influenced the magnitude and direction of pre-dicted change in species distributions under future cli-mates, we compared ‘range’ and ‘core’ areas of currentand future potential distributions that had been derivedusing (a) all 35 parameters, (b) ‘customised’ sets ofparameters selected for individual species, and (c) the‘generalised’ set of parameters.

3.4.1. ‘Range’ areas (0–100 percentile)We defined ‘range’ areas as locations where the val-

ues of all climatic parameters fell within the 0–100percentile range of a species climatic envelope. Mostspecies were predicted to suffer contractions in theirdistributions under climate change, regardless of thescenario used or the method of choosing parameters.For the worst-case scenario, the average reduction in‘range’ areas was−12.6% when the full set of pa-rameters was used,−11.4% for the ‘customised’ set,and−15.7% for the ‘generalised’ set of parameters.In general, there were no significant differences in theproportional change of future ‘range’ areas predictedby the three methods of selecting parameters (Table 5,ANOVA worst-case scenario: d.f. = 2, 72,P= 0.07,n= 24; best-case scenario: d.f. = 2, 72,P= 0.09,n= 24).Thus, qualitative conclusions about the potential im-pact of climate change on distributions were not greatlya

id-u oiceh ions(

Table 5Average percent change in range and core regions of 25 butterfly sp ompared tocurrent potential distributions

Climate change scenario % Change in number of grid ce anges

All parameters Custo

CSIRO range −12.6 (7.2) −11.4 (CSIRO core −13.8 (9.5) −13.8 (DARLAM range −4.7 (3.9) −5.1 (DARLAM core −5.3 (4.6) −7.0 (

S.D. in parenthesis. Species distributions were modeled using three d sed all 35parameters, ‘customised’ used parameters selected for each individu ge of changesis the difference between the greatest and smallest range change for

ffected by the method of parameter selection.When examining the predictions for some indiv

al species, however, the method of parameter chad a more substantial influence on the predictFig. 8). An extreme example is that ofPhilliris nitens,

ecies distributions for 2 climate change scenarios for 2020, c

lls Range of ch

mised set General set Mean range

7.8) −15.7 (6.9) 8.6 (8.8)9.8) −15.1 (10.1) 8.9 (8.5)4.8) −6.8 (4.6) 6.5 (5.5)5.5) −6.8 (5.4) 6.3 (3.5)

ifferent sets of climatic parameters available in BIOCLIM. ‘All’ ual species, and ‘general’ used a set of 8 parameters. Mean raneach species, averaged across all species.


whose distribution under the worst-case scenario in2020 was predicted to decrease by 33 and 30% us-ing the full set of parameters and the ‘customised’ set,respectively, but by only 4% using the ‘generalised’ set.

For a minority of species, the direction of change inthe size of their distribution was also somewhat depen-dent on parameter choice (Fig. 8a–d). Under the worst-case scenario, a reversal in the direction of changeof ‘range’ areas occurred for two species. Predictions

for changes in ‘range’ area ofJalmenus iciliusvariedfrom +1.8 to−6%, using all parameters and the ‘gen-eralised’ set, respectively. The difference was greaterfor Pantoporia consimilis, whose distributions variedfrom −30% when the ‘generalised’ set of parameterswas applied, to +9% when this species’ ‘customised’set of parameters was used (Fig. 9). Under the best-casescenario, reversals in the direction of change occurredfor six species. For four of these species, the difference

Fdpao

ig. 8. Percent change in the size of 25 butterfly species distributionsistributions. Species distributions were modeled using three differearameters, ‘general’ used a set of eight parameters, ‘customised’ uss locations where the values of all parameters lay within the 9th andrder as they appear inTable 2.

for two climate change scenarios for 2020, compared to current potentialnt sets of climatic parameters available in BIOCLIM. ‘All’ used all 35ed parameters selected for each individual species. Core areas are defined

95th percentiles of the species envelope. Species are displayed in the same


Fig. 8. (Continued.)

between the greatest increase and decrease in range sizewas less than 10%. Again,P. consimilisrepresented themost extreme case. This species’ distribution was pre-dicted to decrease by 1 and 18% when modeled with allparameters and the ‘generalised’ set, respectively, butto increase by 7% when modeled using its ‘customised’set of parameters.

3.4.2. ‘Core’ areas (5–95th percentile)We defined core regions as cells where the value

of each parameter fell within the 5th and 95th per-

centiles of the species’ envelope. In general, there wereno significant differences in the proportional changeof ‘core’ areas predicted by the three methods of se-lecting parameters (Table 5, ANOVA CSIRO: d.f. = 2,72,P= 0.61,n= 24; DARLAM: d.f. = 2, 72,P= 0.37,n= 24). For the worst-case scenario, the average reduc-tion in ‘core’ areas was−13.8% when both the full setof parameters and the ‘customised’ set were used, and−15.1% for the ‘generalised’ set of parameters. Underthe worst-case scenario, the direction of change was re-versed for 2 of the 25 species (P. consimilis,O.walkeri),


Fig. 9. (a) Current potential distribution predicted forPantoporia consimilisusing all 35 parameters in BIOCLIM, compared to future distributionspredicted under the worst-case climate change scenario, using (b) all 35 parameters, (c) ‘customised’ set of parameters for this species, and (d)the ‘general’ set of parameters applied to all butterfly species. Dark gray areas are ‘core’ regions, while light gray areas are ‘range’ regions.

depending on which parameters were selected. Underthe best-case scenario, the direction of change differedfor eight species (Fig. 8). The most extreme responsewas that ofT. eliena, whose distribution was predictedto increase very slightly using all parameters, and to de-crease by 11% using its ‘customised’ set of parameters.

4. Discussion

While the advantages and limitations of bioclimaticmodels have been discussed in depth byPearson andDawson (2003), Baker et al. (2000), andFerrier andWatson (1997), the effect that inclusion or exclusionof different climatic parameters has on the predictionsgenerated has received little attention. This is an impor-tant consideration because at present, predictive mod-els such as BIOCLIM are the most widely used method

for estimating future changes in species distributions.Furthermore, their output is increasingly being used tohelp guide conservation decisions (e.g.Rutherford etal., 1999; Dockerty et al., 2003; Hossell et al., 2003;Tellez-Valdes and Davila-Aranda, 2003), and to iden-tify species most at risk of extinction (Busby, 1988;Brereton et al., 1995; Beaumont and Hughes, 2002;Thomas et al., 2004).

4.1. Selection of climatic parameters

Previous studies using BIOCLIM have often incor-porated all or many of the climatic parameters thatwere available in the version used (Bennett et al., 1991;Panetta and Mitchell, 1991; Law, 1994; Backhouse andBurgess, 1995; Brereton et al., 1995; Martin, 1996;Jackson and Claridge, 1999). However, this may leadto over-fitting as progressive addition of parameters in


BIOCLIM results in increasingly narrower potentialdistributions.

In this study, we compared four methods of select-ing climatic parameters available in the current versionof BIOCLIM; (a) using all 35 parameters, (b) select-ing a ‘customised’ set of parameters relevant for in-dividual species by examining frequency distributionhistograms BIOCLIM creates for all parameters, (c)creating a ‘generalised’ set based on parameters mostcommonly classified as relevant, and (d) randomly se-lecting parameters.

The interpretation of histograms is subjective, andmay be influenced by the number of location recordsfor a species. This occurs because a smaller samplesize results in histograms for which no distribution pat-tern can be determined. Selection of too few parame-ters can substantially increase commission errors (i.e.false positives). For example,O. latialis is restricted tothe tablelands and mountains of Southeast Australia,and is found at altitudes above 1000 m in New SouthWales and the Australian Capital Territory, and above1200 m in Victoria (Braby, 2000). The current poten-tial distribution estimated for this species using all 35climatic parameters does not extend past its presentnorth and south range margins (which span less than4◦ latitude). However, using its customised set of fiveparameters selected through histogram analysis, the po-tential latitudinal range ofO. latialis spans almost 14◦latitude, from the Queensland/New South Wales bor-der to southern Tasmania. Even if locations where atl per-c ciese nges is-t hlyu er-r -s thatd arem tersm stri-b ione , butw ionsp tersa ac-c rama

When changes in the distributions of a number ofspecies are to be compared, a generalised set of pa-rameters can be applied. For example, in their studyon climate change and Northeastern Australian tropi-cal rainforests,Williams et al. (2003)applied a set of10 parameters, which had previously been shown to ex-plain patterns of biodiversity within the region. In thepresent study, our ‘generalised’ set comprised eight pa-rameters that we had classified as relevant for at leastone-third of the butterfly species, by visually analysinghistograms of each parameter throughout the speciesknown locations. However, as discussed above, prob-lems in histogram analyses may again bias these resultsbecause the choice of parameters is subjective and maybe influenced by the number of location records.

4.2. Over-fitting

While it has been argued that inclusion of furtherparameters provides for a more useful discriminationof potentially suitable habitat (Nix, 1986), we found alevel of redundancy among parameters. While furtheraddition of parameters results in progressively smallerpredicted distributions, distributions predicted using all35 parameters are only 50% smaller than distributionspredicted using 6 random parameters (Fig. 6). Further,different classes of parameters contributed unequallyto the predicted distribution. Temperature and radiationparameters, separately and together, typically resultedin a distribution closer in size to that derived usinga ices.T arida am-p ds , ofv thed ver,w ast-e era-t or ofst -t ce ins

4

me-t dif-

east one parameter fell outside of the 5th and 95thentiles are excluded (i.e. locations where the spexperiences climatic extremes), the latitudinal ratill spanned over 12◦. Such an extensive potential dribution for this localised, sedentary species is hignlikely, and may reflect two types of commissionor: real and apparent (Peterson, 2001). Real commision errors occur when combinations of conditionso not actually influence the species distributionsodeled. In this case, more, or different, parameay be required to predict this species’ potential di

ution accurately. Alternatively, apparent commissrrors represent areas that are climatically suitablehere other factors such as interspecific interactrevent the species living there. If climatic paramere to be selected for individual species, predictiveuracy could be increased by supplementing histognalysis with expert opinion where possible.

ll parameters, than precipitation and moisture indhis result may be different for species inhabitingreas, where moisture may be more limiting. For exle,Dingle et al. (2000)found that annual rainfall anoil moisture explained 90 and 62%, respectivelyariance in the richness of migrant butterflies inry centre of Australia. These two factors howeere not significant for butterflies in the wetter ern areas of Australia. In Eastern Australia, temp

ure seasonality was the best single climatic predictpecies richness. In contrast,Dingle et al. (2000)foundhat measures of rainfall seasonality (Table 1, parameers 13–19) each explained around 70% of varianpecies richness in both regions.

.3. Biogeography

The effectiveness of a ‘generalised set’ of paraers may be diminished when species are found in


ferent habitat types and bioregions. UnlikeWilliams etal. (2003), who used a subset of 10 parameters to pre-dict the distributions of species endemic to Northeast-ern Australia, our ‘generalised set’ of parameters wasfor species that together occupied a number of differ-ent biogeographical regions. We found low similarityin the selection of climatic parameters between specieswithin each zone (Table 4), and significant differencesbetween species in 4 of the 10 possible pairs of bio-geographical regions. Again, these results may be in-fluenced by the number of locations from which eachspecies had been recorded, and hence the number ofclimatic parameters selected as relevant.

4.4. Effects of number of location records

The number of location records can affect predicteddistributions in two ways. First, low numbers ofrecords can lead to difficulties in interpreting patternsof histograms. Second, biases can occur if speciesdistributions are insufficiently sampled. As a result,climatic envelopes may be incomplete, and theaccuracy of predicted distributions will be decreased.This could be seen by generating climatic envelopesand predicted distributions for five species usingonly one-third of each species’ location records (i.e.a decrease to between 30 and 121 observations perspecies). On average, distributions decreased by 45%when the subset of location records were used. Thisresult differs from that ofKadmon et al. (2003), whosea od-e cientt tp led,B ef ciesh aticp grama henu in ana s.

4

as-s mayc hate ver-

fitting, rather than an indication of real change. Thisstudy showed that the method of selecting climaticparameters in BIOCLIM did not have a significantimpact on theaveragemagnitude of change in thesize of ‘range’ and ‘core’ regions of species’ futuredistributions.

For the worst-case scenario, changes in speciesdistributions predicted under climate change were, onaverage, greater than the variability in range size thatoccurred as a result of different methods of selecting pa-rameters (Table 5,Fig. 8a–d). Out of a total of 100 com-binations (i.e. 25 species× 2 climate change scenarios,modeled for both ‘core’ and ‘range’ regions = 100),the proportional difference between current and futuredistributions across the three methods was less than10% for 79 cases, and greater than 15% for only 7cases.

For a minority of species, however, the direction ofchange in future distributions compared to current oneswas reversed, depending on the method of parameterselection. Under the worst-case scenario, such rever-sals in the direction of change of ‘core’ and ‘range’ re-gions occurred for only 2 of the 25 species. Under thebest-case scenario, reversals occurred within the ‘core’regions for eight species distributions, and ‘range’ re-gions for six species.

5. Conclusions

htt renta nd-i eciesd o-v -t e, ifr n de-t shipb thep

ur-r ec-o

( tiontest

nalysis of the performance of climatic envelope mls suggested that 50–75 observations were suffi

o obtain maximal accuracy.Busby (1991)stated tharovided major bioclimatic gradients were sampIOCLIM is not very sensitive to sampling bias. W

ound that although some widely distributed spead large numbers of location records, few climarameters were selected as relevant via histonalyses. Unfortunately, this can be a problem wsing museum records that have been collectedd hoc manner rather than from systematic survey

.5. Climate change

As bioclimatic models are frequently used toess the extent to which species’ distributionshange in the future an important question is to wxtent predictions may be an artefact of model o

This study used the program BIOCLIM to highlighe extent to which predictions about the size of curnd future distributions of species may differ depe

ng on the number of parameters used to model spistributions. We found that although BIOCLIM prides a useful tool forgeneralisingabout the potenial responses of multiple species to climate changesponses of specific species are to be studied iail, greater emphasis must be given to the relationetween the selection of climatic parameters andredictions generated.

As BIOCLIM is often chosen to model species cent or future distributions, we make the following rmmendations:

1) Consideration should be applied in the selecof parameters, to identify those that have greapredictive power, and to minimise errors.


(2) Histograms for each parameter for each speciesshould be examined, as well as the range of valueswithin the species envelope and across the studyarea, to remove parameters that appear to have poorpredictive power.

(3) Expert opinion and knowledge of the biology of thespecies should be used as much as possible duringparameter selection.

(4) When producing climate change predictions, dif-ferent combinations of the parameters can be usedin a sensitivity-type analysis to produce a range ofpredictions. This is analogous to assessing distri-bution changes across a range of climate changescenarios.

In conclusion, our results highlight several impor-tant points applicable not just to BIOCLIM but also tobioclimatic models in general:

(1) Although variation does occur in theabsolutesizeof predicted distributions depending on how pa-rameters are selected in BIOCLIM, when averagedover many species the proportional loss or gain ofclimatically suitable habitat is relatively insensi-tive to the number of parameters used to predictdistributions.

(2) If the responses of individual species are to be stud-ied, or actual sizes and locations of distributions arerequired (rather than estimates of % loss or gain),

ibu-

( ingveral

( on-uredi.e.uc-

itat

( ribu-tedthanThisses,pos-

Acknowledgements

We are indebted to Graeme Newell from the ArthurRylah Institute and Steve Williams from the Coopera-tive Research Centre for Tropical Rainforest Ecology,James Cook University, for their valuable advice on theuse of BIOCLIM and comments on an earlier draft ofthis manuscript. We also thank Kevin Hennessey fromCSIRO Division of Atmospheric Science for adviceon climate change scenarios, Scott Ginn and MichaelBraby for suggestions as to the relevance of differentclimatic parameters for butterflies, and Daniel Falsterfor computing expertise. Nigel Andrew, David Cheal,Chris Thomas, and Andy Pitman kindly commentedon earlier drafts of this manuscript. This project wasundertaken while L.J.B. was a recipient of an Aus-tralian Postgraduate Award. The OzClim model wasjointly developed by International Global Change In-stitute (IGCI), University of Waikato and CSIRO At-mospheric Research.

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