Use of Climatic Parameters in BIOCLIM and Its Impact on Predictions of Species’ Current and Future Distributions

Ecological Modelling 186 (2005) 250–269

Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions

Linda J. Beaumont a, ∗, Lesley Hughes a, Michael Poulsen b

a Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia b Department of Human Geography, Macquarie University, NSW 2109, Australia Received 9 May 2004; received in revised form 11 January 2005; accepted 17 January 2005 Available online 17 February 2005

Abstract

Bioclimatic models are widely used tools for assessing potential responses of species to climate change. One commonly used model is BIOCLIM, which summarises up to 35 climatic parameters throughout a species’ known range, and assesses the climatic suitability of habitat under current and future climate scenarios. A criticism of BIOCLIM is that the use of all 35 parameters may lead to over-fitting of the model, which in turn may result in misrepresentations of species’ potential ranges and to the loss of biological reality. In this study, we investigated how different methods of combining climatic parameters in BIOCLIM influenced predictions of the current distributions of 25 Australian butterflies species. Distributions were modeled using three previously used methods of selecting climatic parameters: (i) the full set of 35 parameters, (ii) a customised selection of the most relevant parameters for individual species based on analysing histograms produced by BIOCLIM, which show the values for each parameter at all of the focal species known locations, and (iii) a subset of 8 parameters that may generally influence the distributions of butterflies. We also modeled distributions based on random selections of parameters. Further, we assessed the extent to which parameter choice influenced predictions of the magnitude and direction of range changes under two climate change scenarios for 2020. We found that the size of predicted distributions was negatively correlated with the number of parameters incorporated in the model, with progressive addition of parameters resulting in progressively narrower potential distributions. There was also redundancy amongst some parameters; distributions produced using all 35 parameters were on average half the size of distributions produced using only 6 parameters. The selection of parameters via histogram analysis was influenced, to an extent, by the number of location records for the focal species. Further, species inhabiting different biogeographical zones may have different sets of climatic parameters limiting their distributions; hence, the appropriateness of applying the same subset of parameters to all species may be reduced under these situations. Under future climates, most species were predicted to suffer range reductions regardless of the scenario used and the method of parameter selection. Although the size of predicted distributions varied considerably depending on the method of selecting parameters, there were no significant differences in the proportional change in range size between the three methods: under the worst-case scenario, species’ distributions decrease by an average of 12.6, 11.4, and 15.7%, using all parameters, the ‘customised set’, and the ‘general set’ of parameters, respectively.

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

However, depending on which method of selecting parameters was used, the direction of change was reversed for two species under 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 is relatively insensitive to the number of parameters used to predict distributions with BIOCLIM. However, when measuring the response 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 cultivation (Jovanovic et al., 2000; Cunningham et al., 2002). Importantly, species distribution models are cur- Over the past century, global average surface tem- rently the only means by which we can assess the poten- perature has increased approximately 0.6 ◦C(IPCC, tial magnitude of changes in the distributions of multi- 2001). There is a growing body of literature revealing ple species in response to climate change (e.g. Brereton consistent responses of plants and animals to the tem- et al., 1995; Eeley et al., 1999; Beaumont and Hughes, perature increase experienced so far (Parmesan et al., 2002; Berry et al., 2002; Erasmus et al., 2002; Midgley 1999; Pounds et al., 1999; Thomas and Lennon, 1999; et al., 2002; Peterson et al., 2002; Peterson, 2003; Hughes, 2000; Kiesecker et al., 2001; McCarthy, 2001; Williams et al., 2003; Meynecke, 2004; Thomas et al., Thomas et al., 2001; McLaughlin et al., 2002; Walther 2004). Recently, distribution models have been used to et al., 2002; Forister and Shapiro, 2003; Hughes, 2003; assess the feasibility of current conservation strategies Parmesan and Yohe,2003; Root et al., 2003; Stefanescu and the value of existing reserves in Great Britain et al., 2003). In a meta-analysis of more than 1700 under future climate scenarios (Dockerty et al., 2003; species, Parmesan and Yohe (2003) found that recent Hossell et al., 2003) and to examine the effects that dif- biological trends such as range shifts and advancement ferent climate regimes may have on biodiversity within of spring events are consistent with predictions of re- existing South African National Parks (Rutherford et sponses to global warming; they conclude that there is a al., 1999). The output of these models has also been very high level of confidence that global warming has used to estimate extinction probabilities of species in already affected organisms. The IPCC has predicted response to global warming (Thomas et al., 2004). that by the end of this century, average temperature Predicting the current or future distributions of increase could be as high as 6 ◦C(IPCC, 2001). As species has principally been conducted using biocli- some species have already responded to a temperature matic models that assume that climate ultimately re- increase of 0.6 ◦C, it is clear that more substantial ef- stricts species distributions. These models summarise fects on species and ecosystems will occur in the future a number of climatic variables within the known range (Root et al., 2003). of a species, thus generating a ‘bioclimatic envelope’. To understand the impacts of future climate change, The models can then be used to (a) identify the species it is imperative that we can confidently predict the current potential distribution, that is, all areas with cli- current and future potential distributions of species. matic values within the species bioclimatic envelope Species distribution models have a broad range of and (b) assess whether these areas will remain climat- applications, and have been used to assess the potential ically suitable under future climate scenarios. threat of pests or invasive species (Ungerer et al., 1999; While criticisms have been leveled at bioclimatic Sutherst et al., 2000), to obtain insights into the bi- models due to their exclusion of biotic interactions and ology and biogeography of species (Anderson et al., dispersal scenarios (Davis et al., 1998), these models 2002; Steinbauer et al., 2002), to identify hotspots of play a vital role in assessing potential distributions endangered species (Godown and Peterson, 2000)or of species (Baker et al., 2000; Pearson and Dawson, predict biodiversity (Maes et al., 2003), to prioritise ar- 2003), and are useful ‘first filters’ for identifying eas for conservation (Chen and Peterson, 2002), and to locations and species that may be most at risk from a establish suitable locations for species translocations or changing 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 of may place unrealistic constraints on identifying climat- examining potential distributions of species for a ically suitable habitat. Similarly, parameters that may in number of reasons. First, the cost of field surveys fact limit a species distributions are excluded from the to assess species distributions can be prohibitive, model, the predicted distributions may have increased especially if a large number of species is involved: commission error rates, i.e. the species is predicted to bioclimatic models can be used to extrapolate habitat- occur in a given location when in fact it does not (for specific information from one region to another to a discussion of prediction errors see Fielding and Bell, assess the likelihood of the presence of a species 1997). Hence, the number of parameters included in a or multiple species. Second, when little is known model is an important consideration because using too about the ecology and biology of a species, such few, or too many parameters, may result in incorrect models provide the only method of estimating cur- predicted distributions. This could lead to inaccurate rent and future potential distributions (Baker et al., identification of species at risk and, subsequently, to 2000). Finally, a large number of datasets, such as those unsound management decisions. Further, the extent to derived from museum and herbaria records, consist of which errors in over- or under-estimating current po- presence-only data (Austin, 1994). While these data tential distributions may propagate under models of fu- cannot be easily examined using conventional spatial ture climates is unknown. A clear understanding of the statistics, they are ideally suited for some types of bio- relationship between over-fitting and the magnitude of climatic models (Burgman and Lindenmayer, 1998; predicted range changes under future climate change Kadmon et al., 2003). Consequently, bioclimatic scenarios is necessary if predictions from BIOCLIM models are an important and widely used tool for and other related models are to be useful, credible man- assessing the potential responses of species to climate agement tools. Therefore, the aims of this study were: change (Guisan and Zimmermann, 2000). (1) To investigate how different methods of combining BIOCLIM (developed conceptually by Nix, 1986) climatic parameters within BIOCLIM may influ- and related GIS approaches, have been widely used ence the predicted distributions of 25 Australian to generate bioclimatic profiles and to assess the butterfly species. current and future potential distributions of a wide (2) To determine the extent to which the selection of range of taxa in Australia, South Africa, and South climatic parameters influenced the magnitude and America (Campbell et al., 1999; Eeley et al., 1999; direction of predicted changes in range under fu- Jackson and Claridge, 1999; Dingle et al., 2000; Doran ture climate change scenarios. Specifically, as con- and Olsen, 2001; Fischer et al., 2001; Backhouse and tractions of species ranges in response to global Burgess, 2002; Beaumont and Hughes, 2002; Claridge, warming may increase the likelihood of extinction 2002; Cunningham et al., 2002; Steinbauer et al., 2002; (Thomas et al., 2004), we assess the extent to which Loiselle et al., 2003; Tellez-Vald´ es´ and Davila-Aranda,´ predictions of habitat loss under global warming 2003; Williams et al., 2003; Meynecke, 2004; Walther scenarios may be an artefact of over-fitting. et al., 2004). Although BIOCLIM can interpolate up (3) To investigate the role that biogeography may play to 35 climatic parameters to define a species climatic in the selection of the most appropriate group of envelope and to predict its potential distribution, the parameters. progressive addition of climatic parameters results in (4) To investigate the extent to which the number of progressively smaller potential distributions. Further, known locations of a species can influence the se- it has been argued that the inclusion of large numbers lection of climatic parameters and the size of pre- of parameters in models such as BIOCLIM may lead dicted distributions. to misrepresentations of the potential distribution of species (Kriticos and Randall, 2001; Chilcott et al., 2. Methods 2003; Williams et al., 2003). For example, inclusion of unnecessary parameters may result in areas being 2.1. BIOCLIM classified as climatically unsuitable when in fact the species could occur there (omission errors). This could BIOCLIM is a correlative modeling tool that inter- occur because inclusion of unnecessary parameters polates up to 35 climatic parameters (Table 1) for any L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 253

Table 1 the species may occur under alternate climate scenar- Bioclimatic parameters used in BIOCLIM v5.1 ios. The program interpolates a species bioclimatic en- 1 Annual mean temperature velope, which is a summary of the climate at locations 2 Mean diurnal range from where the species has been recorded. BIOCLIM 3 Isothermality is a range-based model that describes a species climatic 4 Temperature seasonality 5 Max temperature of warmest period envelope as a rectilinear volume (Fig. 1), that is, it sug- 6 Min temperature of coldest period gests that a species can tolerate locations where values 7 Temperature annual range of all climatic parameters fit within the extreme values 8 Mean temperature of wettest quarter determined by the set of known locations (Carpenter 9 Mean temperature of driest quarter et al., 1993). The current potential distribution of a 10 Mean temperature of warmest quarter 11 Mean temperature of coldest quarter species is identified by interpolating the climate within 12 Annual precipitation each grid cell of a Digital Elevation Model (DEM) and 13 Precipitation of wettest period comparing it to the climatic profile of the species. Lo- 14 Precipitation of driest period cations with values of all climatic parameters within 15 Precipitation seasonality the range of the species profile are classified by BIO- 16 Precipitation of wettest quarter 17 Precipitation of driest quarter CLIM as climatically suitable. However, multiple lev- 18 Precipitation of warmest quarter els of classification can be achieved by removing the 19 Precipitation of coldest quarter extreme values of each parameter, and identifying loca- 20 Annual mean radiation tions with climatic values that lie within different per- 21 Highest period radiation centile limits. For example, locations where the values 22 Lowest period radiation 23 Radiation seasonality of all parameters lie within the 5–95th percentiles of 24 Radiation of wettest quarter the species envelope may be classified as ‘core’ regions 25 Radiation of driest quarter (Fig. 1). 26 Radiation of warmest quarter 27 Radiation of coldest quarter 28 Annual mean moisture index 29 Highest period moisture index 30 Lowest period moisture index 31 Moisture index seasonality 32 Mean moisture index of high quarter 33 Mean moisture index of low quarter 34 Mean moisture index of warm quarter 35 Mean moisture index of cold quarter

location for which the latitude, longitude, and elevation are known (for a full description of BIOCLIM, see Nix, 1986; Houlder et al., 2001). While primarily used in the Southern Hemisphere, BIOCLIM can use climate surfaces generated from meteorological data for any country. For Australia, climate surfaces have been generated from long-term monthly averages of climate Fig. 1. Diagrammatic representation of a hypothetical two- variables at over 900 temperature stations and 11,000 dimensional bioclimatic envelope. Dots represent values of mean precipitation stations throughout the continent (Busby, annual temperature and mean annual precipitation at each known lo- 1991). cation of a hypothetical species. In predicting a species’ potential dis- BIOCLIM can be used for three tasks (a) describ- tribution, BIOCLIM would classify all locations with values within the extremes of the species envelope (unbroken line) as suitable. The ing the environment in which the species has been dashed box represents those areas where climatic values outside of recorded, (b) identifying other locations where the the 5–95th percentiles of the species envelope are excluded. This species may currently reside, and (c) identifying where ﬁgure has been modiﬁed from Carpenter et al. (1993). 254 L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269

Originally, BIOCLIM incorporated 12 climatic Table 2 indices, based on temperature and precipitation. Nix Biogeographical zones for Australian butterﬂy species, for which (1986) argued, however, that while each of the 12 potential distributions were modeled indices provided useful discrimination in particular Species Biogeographical zone applications, the seasonality of temperature and Anisynta dominula Montane precipitation was not adequately described. Hence, Argynnina cryila Southeast Australia additional indices were incorporated into later ver- Candalides erinus Broadly distributed Delias nysa East coast sions of the program, resulting in a more complete Dispar compacta Southeast Australia description of the bioclimatic envelope. These include Elodina queenslandica Northeast Queensland the coefﬁcient of variation of monthly temperature and Hesperilla donnysa Broadly distributed of precipitation, a measure of isothermality, and the Heteronympha paradelpha Southeast Australia mean temperature and precipitation for the warmest Hypocysta metirius East coast Jalmenus icilius Broadly distributed and coldest quarters. Within BIOCLIM (v5.1), the user Mesodina halyzia East coast can select which of the climatic parameters to include Neolucia hobartensis Montane when identifying suitable habitat. The disadvantage of Netrocoryne repanda East coast using less than the full set of parameters is that some Ocybadistes walkeri Broadly distributed possible interactions and partial substitutions between Ogyris amaryllis Broadly distributed Oreisplanus munionga Montane indices may be excluded (Martin, 1996). For example, Oreixenica latialis Montane although an area may have low rainfall, this may be Oreixenica orichora Montane compensated to an extent by lower evaporation, which Pantoporia consimilis Northeast Queensland in turn will depend upon temperature and radiation Paralucia aurifer Southeast Australia (Nix, 1986). Therefore, a moisture index was added to Philiris nitens Northeast Queensland Pseudalmenus chlorinda Southeast Australia later versions of BIOCLIM (Martin, 1996). Tagiades japetus Northeast Queensland Tellervo zoilus Northeast Queensland 2.2. Species selection Trapezites eliena East coast

We selected 25 species of butterflies from five biogeographic zones in Australia (Montane, Northeast in mountainous regions (D. Houlder, personal commu- Queensland, East coast, Southeast coast, and those nication). broadly distributed across Australia, n = 5 in each zone), for which we had records from at least 50 unique 2.3. Current potential distributions locations (Table 2, Fig. 2). Species distributions were obtained from the Dunn & Dunn National Database We produced current potential distributions for each of Australian Butterflies, which contains over 110,000 of the 25 butterfly species using 4 different methods of records of the collection locations of butterflies, com- selecting climatic parameters. piled from public and private collections, and from the literature (Dunn and Dunn, 1991). Distributions 2.3.1. Full set of all species were mapped in ArcView v3.2 (ESRI, Potential distributions were produced using all 35 2000) and compared to maps in Butterflies of Australia climatic parameters available in BIOCLIM v5.1. To- (Braby, 2000) to identify anomalous points that may gether, these produce the smallest possible distribution have resulted from incorrect geocoding or identifica- for each species predicted by the program. tion. Questionable locations were removed from further analysis. 2.3.2. Customised set The elevations of all locations were derived using a For each species, we produced a ‘customised’ set Digital Elevation Model, Aus40.DEM (CRES, 1999), of parameters that we considered most relevant for which has a resolution of 1/40 of a degree (approxi- each individual species. Selection of these parameters mately, 2.5 km grid squares). This DEM has an accu- would ideally be based on knowledge of the biology racy of ±10 m in relatively flat topography and ±100 m of the species in question. However, when the biology L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 255

Fig. 2. Locations of biogeographical zones from which 25 Australian butterﬂy species used in this study occur. Broadly distributed species were found 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 with in the driest quarter throughout the known range of many species, these parameters can be selected based Netrocoryne repanda is normally distributed, with val- on histograms produced by BIOCLIM, which show ues throughout the species distribution ranging from 0 the frequency distribution of values of each climatic to 267 mm. This species is found along the East coast parameter throughout the species’ known range. For of Australia, from Cape York to Victoria. However, as example, it can be hypothesised that parameters with the precipitation of the driest quarter throughout most normally distributed values may be an important in- of Australia is within the range to which the species fluence on the species distribution (Fig. 3a). Similarly, is currently exposed, it is unlikely that this parameter parameters that are highly skewed may also be relevant is limiting the distribution of N. repanda. Hence, this to the species distribution (Fig. 3b). Parameters with parameter was not used to predict this species potential skewed distributions may be those that do not have distribution. a negative value, such as rainfall, and those that have We visually examined the histograms of each of values between zero and one, such as moisture indices. the 35 climatic parameters for each of the 25 species, Where there is no clear pattern in the histograms for a and subjectively classified the parameter as relevant or parameter, that parameter could be classified as irrel- not, thus identifying a ‘customised’ set of parameters evant, i.e. it does not appear to influence the species relevant for each species. Potential distributions were distribution (Fig. 3c). Similarly, where the histogram is derived for each species using their ‘customised’ set normally distributed but is truncated in one or both tails, of parameters. The number of climatic parameters the parameter could also be rejected, as these graphs selected for each species ranged from 3 to 16, with an suggest that the species could tolerate other values of average of 7 (±S.D. 3). this parameter that were not included in the species climatic envelope (Fig. 3d). This may occur if the 2.3.3. Generalised set distribution records for a species do not cover its entire An alternative method of selecting climatic param- geographic range. The usefulness of individual parameters in BIOCLIM is to identify a subset of parameters eters can also be assessed by comparing the values of applicable to the taxon or habitat in question, and a parameter within the species envelope to the range apply these to all species (e.g. Williams et al., 2003). of values of that parameter across the study region. We identified which of the 35 parameters we had clas- For example, a histogram of the values of precipitation sified as relevant in the ‘customised’ sets for at least 256 L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269

Fig. 3. Examples of frequency distribution histograms produced by BIOCLIM for values of climatic parameters throughout the network of a species’ 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 fulfilled 2.3.4. Random set this criteria, and this comprised our ‘generalised’ For five randomly selected species (Anisynta set: annual mean temperature, mean diurnal range, dominula, Oreixenica latialis, Hypocysta metirius, max temperature of warmest period, min temperature N. repanda, Heteronympha paradelpha), we created of coldest period, temperature annual range, mean potential distributions using random sets of climatic temperature of warmest quarter, mean temperature parameters. We generated sets of parameters com- of coldest quarter, and annual precipitation. We prising 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, modeled potential distributions for each species using 27, 29, 31, and 33 parameters each. Five of each set this set. of parameters were generated (i.e. a total of 85 sets We calculated the percent increase in the size of of parameters). For each of the 5 species, we used species current potential distributions using (a) the each of the 85 sets of random parameters to create ‘customised’ set of parameters and (b) the ‘generalised’ new potential distributions. We calculated the mean set of parameters, compared to distributions for each increase in the size of potential distributions derived species based on all 35 parameters. Linear regressions from each set of random parameters for each species, were used to assess the relationship between the change compared to distributions derived using all 35 climatic in size of these distributions and the number of climatic parameters. The relationship between the mean parameters selected. percent change in distribution size versus the number L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 257 of climatic parameters was assessed using logarithmic process of analysing the histograms, selecting relevant regression. parameters, and modeling potential distributions. To determine whether different classes of parameters contributed more to the size of current potential 2.6. Climate change scenarios distributions than others, we created current potential distributions for each of the five randomly selected To assess whether the method of selecting climatic species using (a) temperature parameters only, (b) parameters influenced the magnitude and/or direction precipitation parameters only, (c) radiation parameters of change of predicted distributions under future cli- only, (d) moisture index parameters only, (e) all pa- mates, we derived two climate change scenarios for rameters except temperature, (f) all parameters except the year 2020 using OzClim v2.0.1 (CSIRO, 1996). precipitation, (g) all parameters except radiation, These scenarios were developed by the CSIRO At- (h) all parameters except moisture indices, and all mospheric Research Unit and the International Global possible pair-wise combinations of parameter classes. Change Institute. The models cover the range of un- Again, we calculated the change in the size of species certainty associated with future global warming due distributions derived from the above sets of parameters to different greenhouse gas emissions and climate compared to potential distributions derived using all 35 sensitivities (K. Hennessy, personal communication). parameters. The ‘worst-case’ (i.e. the greatest change from current climate) scenario used the Global Circulation 2.4. Effects of biogeography Model (GCM) CSIRO Mk 2, with the SRES scenario A1F and high climate forcing. This model pro- The 25 butterfly species used in this study came from duces a hot/dry response. The ‘best-case’ (the least five different biogeographical zones of Australia (Mon- change from current climate) scenario used the DAR- tane, Northeast Queensland, East coast, Southeast LAM model with the SRES scenario B1 and low coast, and those broadly distributed across Australia, climate forcing, producing a wet/warm response (K. n = 5 in each zone Fig. 2). We hypothesised that the Hennessy, personal communication). Changes in min- application of a generalised set of climatic parameters imum temperature, maximum temperature, and precip- to all species may be inappropriate if the distributions itation were extracted for each 1◦ latitude/longitude of species inhabiting different biogeographical zones cell across Australia. To assess how species distribu- are limited by different climatic parameters. We tions may change, we created new climatic grids in conducted Similarity Percentages (SIMPER) and BIOCLIM that incorporated the changes in temper- Analysis of Similarity (ANOSIM) using Primer v5.2.0 ature and precipitation. For each of the 25 butterfly (Primer-E, 2001) to determine whether the climatic species, we produced future potential distributions us- parameters we had selected for each individual species ing (a) all climatic parameters, (b) the ‘customised’ were similar for species within the same biogeo- set of parameters, and (c) the ‘generalised’ set of graphical zone, and different for species in different parameters. zones. The change in the size of future distributions relative to current ones was calculated for both the ‘range’ and 2.5. Effects of the number of known records ‘core’ regions of each predicted distribution for each species. All locations predicted to contain the species in Our results may have been biased against species question are termed ‘range’ areas, while those locations with fewer distribution records because histogram pat- where the values of parameters lie within the 5th and terns are not always clear when the number of records is 95th percentiles are termed ‘core’ regions. The value low. Hence, we randomly selected five butterfly species of studying ‘core’ regions is that it reduces the effect (Hesperilla donnysa, H. metirius, N. repanda, Oreix- that outliers, or non-representative observations, have enica orichora, Trapezites eliena), and then randomly on the sensitivity of predicted distributions (Kadmon removed two-thirds of their records, leaving between et al., 2003). 30 and 121 records for each species. After re-creating We used ANOVA’s to assess whether the proportion each species’ climatic envelope, we repeated the of loss or gain of future distributions compared to 258 L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 current distributions was similar among the three methods of selecting parameters.

3. Results

3.1. Current potential distributions

3.1.1. Full set When all 35 climatic parameters were included, the mean size of the 25 species distributions was 105,187 ± grid cells ( S.D. 185,089), where each grid cell is Fig. 4. The average size of 25 butterﬂy species distributions (number 1/40th of a degree (Fig. 4). Standard deviations were of grid cells where each cell is 1/40th of a degree latitude/longitude), large, because some species have restricted distribu- predicted using different numbers of climatic parameters in BIO- tions while others are distributed widely. For example, CLIM. All = all 35 parameters, general = a subset of 8 parameters, O. latialis was predicted to have the narrowest distribu- customised = customised sets of parameters used for each species (mean per species = 7 ± 3.6). Bars represent standard deviations. tion, with a total of 3039 cells identiﬁed as climatically suitable, while Ocybadistes walkeri had the largest predicted distribution, covering 334,223 grid cells (Fig. 5). from3to16(N = 25, mean = 7, ±S.D. 3). The mean number of grid cells selected as climatically suitable 3.1.2. ‘Customised’ set was 184,368 (±S.D. 282,101). Distributions were, on The number of climatic parameters selected from average, 2.3 times larger than those derived using all pa- examination of histograms for each species ranged rameters (±S.D. 1.2). Tagiades japetus was predicted

Fig. 5. Comparisons of the (a) known distribution of Ocybadistes walkeri, and the predicted current distributions modeled in BIOCLIM using (b) all 35 climatic parameters, (c) a ‘customised’ set of parameters for this species, and (d) a ‘general’ set of parameters applicable to all butterfly species. Dark gray areas represent ‘core’ regions, while light gray areas represent ‘range’ regions. L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 259 to have the narrowest distribution, with a total of 13,533 further addition of parameters has progressively less cells identified as climatically suitable, while Ogyris effect on the size of predicted distributions. For ex- amaryllis had the largest predicted distribution, cover- ample, on average a distribution predicted from all 35 ing 1,089,072 grid cells. parameters is 40% of the size of a distribution predicted from 5 parameters, 80% of the size of a dis- 3.1.3. ‘Generalised’ set tribution predicted from 15 parameters, and 89% of We visually assessed the kurtosis of histograms that the size of a distribution predicted from 25 parameters BIOCLIM produced of values for each climatic param- (Fig. 6). Furthermore, an average of 67% of cells iden- eter throughout each species known range, and identi- tified as containing climatically suitable habitat using fied 8 parameters that commonly appeared to influence the ‘customised’ method, were also suitable using the the distributions of the 25 butterfly species. These pa- same number of randomly selected climatic parame- rameters were annual mean temperature, mean diurnal ters (±S.D. 16%). Redundancy of parameters was also range, max temperature of warmest period, min tem- shown by including or excluding different classes of pa- perature of coldest period, temperature annual range, rameters (Table 3, Fig. 7). For example, the inclusion mean temperature of warmest quarter, mean tem- of all temperature and radiation parameters but no pre- perature of coldest quarter, and annual precipitation. cipitation or moisture indices, resulted in distributions The mean size of the current distributions using this that were only 16% larger on average than those us- ‘generalised’ set of parameters was 153,126 grid cells ing all 35 parameters. Similarly, the exclusion of either (±232,901), and they were, on average, 1.7 times larger the precipitation or the moisture index parameters did than those derived using all parameters (±S.D. 0.3). O. not change the size of the potential distribution sub- latialis was predicted to have the narrowest distribu- stantially (4 and 5% increase, respectively; Table 3). tion, with a total of 5616 cells identified as climatically Comparisons of groups of parameters suggest that pre- suitable, while O. amaryllis had the largest predicted cipitation and moisture index parameters were not as distribution, covering 945,322 grid cells (Fig. 5). useful for defining the suitability of habitat for the 25 Intuitively, the inclusion of more climatic parame- species, compared to temperature and radiation param- ters will place tighter constraints on classifying habitat eters (Table 3). as climatically suitable, and hence the size of potential distributions will decrease. The percent change in the size of current distributions using the ‘customised’ sets of parameters compared to all parameters (dependent variable), and the number of ‘customised’ parameters for each species (independent variable), was significantly negatively correlated (r2 = 0.38, F = 13.86, P = 0.001, n = 25). Similarly, the percent change in the size of current distributions using the ‘customised’ sets of parameters compared to the ‘general’ set of parameters (dependent variable), and the number of ‘customised’ parameters for each species (independent variable) was also significantly negatively correlated (r2 = 0.46, F = 19.88, P = 0.0001, n = 25).

3.1.4. Random set Fig. 6. The average agreement in the size of distributions for five There was a highly significant negative relationship butterfly species predicted using BIOCLIM by continual addition between progressive addition of random parameters of randomly selected climatic parameters, compared to the size of and the size of species distributions (r2 = 0.98, F = 988, species distributions modeled using all 35 climatic parameters. Bars P < 0.001, n = 16). While addition of parameters leads represent standard deviations. Agreement is defined as the proportion of cells classified as climatically suitable in both the distribution to progressively smaller predicted distributions, there derived using random parameters and that derived using all parame- is a level of redundancy among parameters, whereby ters. 260 L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269

Table 3 for those species found in Northeastern Australia, to Average increase in the predicted distributions of 5 butterfly species 55% for species in the southeast (Table 4). We also derived from subsets of climatic parameters, compared to distribu- compared the differences in parameters classified as tions derived using 35 climatic parameters relevant for species found in different biogeographical Parameter groups % Increase in distribution zones across Australia. Of 10 possible pair-wise size mean (S.D.) comparisons of 5 biogeographical zones, there were All but temperature 76 (119) significant differences in the selection of parameters All but precipitation 4 (3) All but radiation 18 (10) for 4 pairs: Montane and broadly distributed; East All but moisture index 5 (4) coast and broadly distributed; broadly distributed Temperature and precipitation 30 (16) and Southeast coast; Southeast and Northeast coast Temperature and radiation 16 (9) (Table 4). Temperature and moisture 31 (16) Precipitation and radiation 103 (156) 3.3. Effects of the number of known records Precipitation and moisture 239 (396) Radiation and moisture 85 (122) Temperature only 131 (140) There was a weak relationship between the number Precipitation only 447 (739) of climatic parameters selected as relevant after visu- Radiation only 168 (266) ally examining histograms, and the number of location Moisture indices only 380 (619) records for each species (r2 = 0.15, F = 4.11, P = 0.054, n = 25). This reflects the difficulty in interpreting histogram distribution patterns for species with fewer 3.2. Effects of biogeography location records. Similarly, after randomly removing two-thirds of the location records of five species and We assessed whether the parameters selected as re-interpreting their histograms, fewer climatic param- suitable for species in the same biogeographical eters were chosen for four of the five species (H. don- regions of Australia were similar. Generally, the nysa, H. metirius, O. latialis, T. eliena), while for the percent similarity (SIMPER) among butterfly species fifth species (N. repanda) the same number of param- within a particular zone was low, ranging from 22% eters was chosen.

Fig. 7. Comparisons of current potential distributions predicted by BIOCLIM for Anisynta dominula using (a) all 35 climatic parameters, (b) temperature and radiation parameters only, and (c) precipitation and moisture parameters only. Dark gray areas represent ‘core’ regions, while light gray areas represent ‘range’ regions. L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 261

Table 4 3.4. Climate change scenarios (a) Within group similarity (ANOSIM) in climatic parameters classified as relevant for butterfly species in five biogeographical regions To assess how the method of selecting climatic pa- in Australia, (b) between group dissimilarity (SIMPER) for each pair-wise combination of biogeographical regions rameters influenced the magnitude and direction of predicted change in species distributions under future cli- (a) Within group similarity % Similarity mates, we compared ‘range’ and ‘core’ areas of current Montane 34.2 and future potential distributions that had been derived East coast 38.6 Broadly distributed 38.4 using (a) all 35 parameters, (b) ‘customised’ sets of Southeast Australia 55.3 parameters selected for individual species, and (c) the Northeast Queensland 22.0 ‘generalised’ set of parameters.

(b) Between group dissimilarity % Dissimilarity Montane and East coast 73.2 3.4.1. ‘Range’ areas (0–100 percentile) Montane and broadly distributed* 74.5 We defined ‘range’ areas as locations where the val- * East coast and broadly distributed 70.8 ues of all climatic parameters fell within the 0–100 Montane and Southeast Australia 60.0 East coast and Southeast Australia 61.0 percentile range of a species climatic envelope. Most Broadly distributed and Southeast Australia* 60.8 species were predicted to suffer contractions in their Montane and Northeast Queensland 77.3 distributions under climate change, regardless of the East coast and Northeast Queensland 76.2 scenario used or the method of choosing parameters. Broadly distributed and Northeast Queensland 66.9 For the worst-case scenario, the average reduction in Southeast Australia and Northeast Queensland* 70.6 ‘range’ areas was −12.6% when the full set of pa- ∗ P < 0.05. rameters was used, −11.4% for the ‘customised’ set, and −15.7% for the ‘generalised’ set of parameters. The climatic envelope is a summary of climate In general, there were no significant differences in the throughout the species known range. Hence, a climatic proportional change of future ‘range’ areas predicted envelope based on a subset of species records may be by the three methods of selecting parameters (Table 5, expected to have a narrower range of climate values ANOVA worst-case scenario: d.f. = 2, 72, P = 0.07, than an envelope based on all of a species’ location n = 24; best-case scenario: d.f. = 2, 72, P = 0.09, n = 24). records. For the five species in this part of our study, Thus, qualitative conclusions about the potential im- removal of two-thirds of the known locations decreased pact of climate change on distributions were not greatly the range of each climatic variable by an average 9.4% affected by the method of parameter selection. (S.D. 9.6%). As a result, potential distributions derived When examining the predictions for some individ- from a subset of location records were 45% smaller on ual species, however, the method of parameter choice average (±S.D. 19%) than when using the full set of had a more substantial influence on the predictions known locations. (Fig. 8). An extreme example is that of Philliris nitens,

Table 5 Average percent change in range and core regions of 25 butterﬂy species distributions for 2 climate change scenarios for 2020, compared to current potential distributions Climate change scenario % Change in number of grid cells Range of changes

All parameters Customised set General set Mean range CSIRO range −12.6 (7.2) −11.4 (7.8) −15.7 (6.9) 8.6 (8.8) CSIRO core −13.8 (9.5) −13.8 (9.8) −15.1 (10.1) 8.9 (8.5) DARLAM range −4.7 (3.9) −5.1 (4.8) −6.8 (4.6) 6.5 (5.5) DARLAM core −5.3 (4.6) −7.0 (5.5) −6.8 (5.4) 6.3 (3.5)

S.D. in parenthesis. Species distributions were modeled using three different sets of climatic parameters available in BIOCLIM. ‘All’ used all 35 parameters, ‘customised’ used parameters selected for each individual species, and ‘general’ used a set of 8 parameters. Mean range of changes is the difference between the greatest and smallest range change for each species, averaged across all species. 262 L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 whose distribution under the worst-case scenario in for changes in ‘range’ area of Jalmenus icilius varied 2020 was predicted to decrease by 33 and 30% us- from +1.8 to −6%, using all parameters and the ‘gen- ing the full set of parameters and the ‘customised’ set, eralised’ set, respectively. The difference was greater respectively, but by only 4% using the ‘generalised’ set. for Pantoporia consimilis, whose distributions varied For a minority of species, the direction of change in from −30% when the ‘generalised’ set of parameters the size of their distribution was also somewhat depen- was applied, to +9% when this species’ ‘customised’ dent on parameter choice (Fig. 8a–d). Under the worst- set of parameters was used (Fig. 9). Under the best-case case scenario, a reversal in the direction of change scenario, reversals in the direction of change occurred of ‘range’ areas occurred for two species. Predictions for six species. For four of these species, the difference

Fig. 8. Percent change in the size of 25 butterﬂy species distributions for two climate change scenarios for 2020, compared to current potential distributions. Species distributions were modeled using three different sets of climatic parameters available in BIOCLIM. ‘All’ used all 35 parameters, ‘general’ used a set of eight parameters, ‘customised’ used parameters selected for each individual species. Core areas are deﬁned as locations where the values of all parameters lay within the 9th and 95th percentiles of the species envelope. Species are displayed in the same order as they appear in Table 2. L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 263

Fig. 8. (Continued .) between the greatest increase and decrease in range size centiles of the species’ envelope. In general, there were was less than 10%. Again, P.consimilis represented the no signiﬁcant differences in the proportional change most extreme case. This species’ distribution was pre- of ‘core’ areas predicted by the three methods of se- dicted to decrease by 1 and 18% when modeled with all lecting parameters (Table 5, ANOVA CSIRO: d.f. = 2, parameters and the ‘generalised’ set, respectively, but 72, P = 0.61, n = 24; DARLAM: d.f. = 2, 72, P = 0.37, to increase by 7% when modeled using its ‘customised’ n = 24). For the worst-case scenario, the average reduc- set of parameters. tion in ‘core’ areas was −13.8% when both the full set of parameters and the ‘customised’ set were used, and 3.4.2. ‘Core’ areas (5–95th percentile) −15.1% for the ‘generalised’ set of parameters. Under We deﬁned core regions as cells where the value the worst-case scenario, the direction of change was re- of each parameter fell within the 5th and 95th per- versed for 2 of the 25 species (P.consimilis, O. walkeri), 264 L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269

Fig. 9. (a) Current potential distribution predicted for Pantoporia consimilis using all 35 parameters in BIOCLIM, compared to future distributions predicted 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. Under for estimating future changes in species distributions. the best-case scenario, the direction of change differed Furthermore, their output is increasingly being used to for eight species (Fig. 8). The most extreme response help guide conservation decisions (e.g. Rutherford et was that of T. eliena, whose distribution was predicted al., 1999; Dockerty et al., 2003; Hossell et al., 2003; to increase very slightly using all parameters, and to de- Tellez-Vald´ es´ and Davila-Aranda,´ 2003), and to iden- crease by 11% using its ‘customised’ set of parameters. tify species most at risk of extinction (Busby, 1988; Brereton et al., 1995; Beaumont and Hughes, 2002; Thomas et al., 2004). 4. Discussion 4.1. Selection of climatic parameters While the advantages and limitations of bioclimatic models have been discussed in depth by Pearson and Previous studies using BIOCLIM have often incor- Dawson (2003), Baker et al. (2000), and Ferrier and porated all or many of the climatic parameters that Watson (1997), the effect that inclusion or exclusion were available in the version used (Bennett et al., 1991; of different climatic parameters has on the predictions Panetta and Mitchell, 1991; Law, 1994; Backhouse and generated has received little attention. This is an impor- Burgess, 1995; Brereton et al., 1995; Martin, 1996; tant consideration because at present, predictive mod- Jackson and Claridge, 1999). However, this may lead els such as BIOCLIM are the most widely used method to over-ﬁtting as progressive addition of parameters in L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 265

BIOCLIM results in increasingly narrower potential When changes in the distributions of a number of distributions. species are to be compared, a generalised set of pa- In this study, we compared four methods of select- rameters can be applied. For example, in their study ing climatic parameters available in the current version on climate change and Northeastern Australian tropi- of BIOCLIM; (a) using all 35 parameters, (b) select- cal rainforests, Williams et al. (2003) applied a set of ing a ‘customised’ set of parameters relevant for in- 10 parameters, which had previously been shown to ex- dividual species by examining frequency distribution plain patterns of biodiversity within the region. In the histograms BIOCLIM creates for all parameters, (c) present study, our ‘generalised’ set comprised eight pa- creating a ‘generalised’ set based on parameters most rameters that we had classified as relevant for at least commonly classified as relevant, and (d) randomly se- one-third of the butterfly species, by visually analysing lecting parameters. histograms of each parameter throughout the species The interpretation of histograms is subjective, and known locations. However, as discussed above, prob- may be influenced by the number of location records lems in histogram analyses may again bias these results for a species. This occurs because a smaller sample because the choice of parameters is subjective and may size results in histograms for which no distribution pat- be influenced by the number of location records. tern can be determined. Selection of too few parameters can substantially increase commission errors (i.e. 4.2. Over-fitting false positives). For example, O. latialis is restricted to While it has been argued that inclusion of further the tablelands and mountains of Southeast Australia, parameters provides for a more useful discrimination and is found at altitudes above 1000 m in New South of potentially suitable habitat (Nix, 1986), we found a Wales and the Australian Capital Territory, and above level of redundancy among parameters. While further 1200 m in Victoria (Braby, 2000). The current poten- addition of parameters results in progressively smaller tial distribution estimated for this species using all 35 predicted distributions, distributions predicted using all climatic parameters does not extend past its present 35 parameters are only 50% smaller than distributions north and south range margins (which span less than predicted using 6 random parameters (Fig. 6). Further, 4◦ latitude). However, using its customised set of five different classes of parameters contributed unequally parameters selected through histogram analysis, the po- to the predicted distribution. Temperature and radiation tential latitudinal range of O. latialis spans almost 14◦ parameters, separately and together, typically resulted latitude, from the Queensland/New South Wales bor- in a distribution closer in size to that derived using der to southern Tasmania. Even if locations where at all parameters, than precipitation and moisture indices. least one parameter fell outside of the 5th and 95th per- This result may be different for species inhabiting arid centiles are excluded (i.e. locations where the species areas, where moisture may be more limiting. For exam- experiences climatic extremes), the latitudinal range ple, Dingle et al. (2000) found that annual rainfall and still spanned over 12◦. Such an extensive potential dis- soil moisture explained 90 and 62%, respectively, of tribution for this localised, sedentary species is highly variance in the richness of migrant butterflies in the unlikely, and may reflect two types of commission er- dry centre of Australia. These two factors however, ror: real and apparent (Peterson, 2001). Real commis- were not significant for butterflies in the wetter east- sion errors occur when combinations of conditions that ern areas of Australia. In Eastern Australia, tempera- do not actually influence the species distributions are ture seasonality was the best single climatic predictor of modeled. In this case, more, or different, parameters species richness. In contrast, Dingle et al. (2000) found may be required to predict this species’ potential distri- that measures of rainfall seasonality (Table 1, parame- bution accurately. Alternatively, apparent commission ters 13–19) each explained around 70% of variance in errors represent areas that are climatically suitable, but species richness in both regions. where other factors such as interspecific interactions prevent the species living there. If climatic parameters 4.3. Biogeography are to be selected for individual species, predictive accuracy could be increased by supplementing histogram The effectiveness of a ‘generalised set’ of parame- analysis with expert opinion where possible. ters may be diminished when species are found in dif- 266 L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 ferent habitat types and bioregions. Unlike Williams et fitting, rather than an indication of real change. This al. (2003), who used a subset of 10 parameters to pre- study showed that the method of selecting climatic dict the distributions of species endemic to Northeast- parameters in BIOCLIM did not have a significant ern Australia, our ‘generalised set’ of parameters was impact on the average magnitude of change in the for species that together occupied a number of differ- size of ‘range’ and ‘core’ regions of species’ future ent biogeographical regions. We found low similarity distributions. in the selection of climatic parameters between species For the worst-case scenario, changes in species within each zone (Table 4), and significant differences distributions predicted under climate change were, on between species in 4 of the 10 possible pairs of bio- average, greater than the variability in range size that geographical regions. Again, these results may be in- occurred as a result of different methods of selecting pa- fluenced by the number of locations from which each rameters (Table 5, Fig. 8a–d). Out of a total of 100 com- species had been recorded, and hence the number of binations (i.e. 25 species × 2 climate change scenarios, climatic parameters selected as relevant. modeled for both ‘core’ and ‘range’ regions = 100), the proportional difference between current and future 4.4. Effects of number of location records distributions across the three methods was less than 10% for 79 cases, and greater than 15% for only 7 The number of location records can affect predicted cases. distributions in two ways. First, low numbers of For a minority of species, however, the direction of records can lead to difficulties in interpreting patterns change in future distributions compared to current ones of histograms. Second, biases can occur if species was reversed, depending on the method of parameter distributions are insufficiently sampled. As a result, selection. Under the worst-case scenario, such rever- climatic envelopes may be incomplete, and the sals in the direction of change of ‘core’ and ‘range’ re- accuracy of predicted distributions will be decreased. gions occurred for only 2 of the 25 species. Under the This could be seen by generating climatic envelopes best-case scenario, reversals occurred within the ‘core’ and predicted distributions for five species using regions for eight species distributions, and ‘range’ re- only one-third of each species’ location records (i.e. gions for six species. a decrease to between 30 and 121 observations per species). On average, distributions decreased by 45% when the subset of location records were used. This 5. Conclusions result differs from that of Kadmon et al. (2003), whose analysis of the performance of climatic envelope mod- This study used the program BIOCLIM to highlight els suggested that 50–75 observations were sufficient the extent to which predictions about the size of current to obtain maximal accuracy. Busby (1991) stated that and future distributions of species may differ depend- provided major bioclimatic gradients were sampled, ing on the number of parameters used to model species BIOCLIM is not very sensitive to sampling bias. We distributions. We found that although BIOCLIM pro- found that although some widely distributed species vides a useful tool for generalising about the poten- had large numbers of location records, few climatic tial responses of multiple species to climate change, if parameters were selected as relevant via histogram responses of specific species are to be studied in de- analyses. Unfortunately, this can be a problem when tail, greater emphasis must be given to the relationship using museum records that have been collected in an between the selection of climatic parameters and the ad hoc manner rather than from systematic surveys. predictions generated. As BIOCLIM is often chosen to model species cur- 4.5. Climate change rent or future distributions, we make the following rec- ommendations: As bioclimatic models are frequently used to assess the extent to which species’ distributions may (1) Consideration should be applied in the selection change in the future an important question is to what of parameters, to identify those that have greatest extent predictions may be an artefact of model over- predictive power, and to minimise errors. L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269 267

(2) Histograms for each parameter for each species Acknowledgements should be examined, as well as the range of values within the species envelope and across the study We are indebted to Graeme Newell from the Arthur area, to remove parameters that appear to have poor Rylah Institute and Steve Williams from the Coopera- predictive power. tive Research Centre for Tropical Rainforest Ecology, (3) Expert opinion and knowledge of the biology of the James Cook University, for their valuable advice on the species should be used as much as possible during use of BIOCLIM and comments on an earlier draft of parameter selection. this manuscript. We also thank Kevin Hennessey from (4) When producing climate change predictions, dif- CSIRO Division of Atmospheric Science for advice ferent combinations of the parameters can be used on climate change scenarios, Scott Ginn and Michael in a sensitivity-type analysis to produce a range of Braby for suggestions as to the relevance of different predictions. This is analogous to assessing distri- climatic parameters for butterflies, and Daniel Falster bution changes across a range of climate change for computing expertise. Nigel Andrew, David Cheal, scenarios. Chris Thomas, and Andy Pitman kindly commented on earlier drafts of this manuscript. This project was In conclusion, our results highlight several impor- undertaken while L.J.B. was a recipient of an Aus- tant points applicable not just to BIOCLIM but also to tralian Postgraduate Award. The OzClim model was bioclimatic models in general: jointly developed by International Global Change In- stitute (IGCI), University of Waikato and CSIRO At- mospheric Research. (1) Although variation does occur in the absolute size of predicted distributions depending on how parameters are selected in BIOCLIM, when averaged over many species the proportional loss or gain of References climatically suitable habitat is relatively insensitive to the number of parameters used to predict Anderson, R.P., Gomez-Laverde, M., Peterson, A.T., 2002. Geo- graphical distributions of spiny pocket mice in South Amer- distributions. ica: insights from predictive models. Global Ecol. Biogeogr. 11, (2) If the responses of individual species are to be stud- 131–141. ied, or actual sizes and locations of distributions are Austin, M.P., 1994. Data Capability, Sub-project 3, Modelling of required (rather than estimates of % loss or gain), Landscape Patterns and Processes using Biological Data. Di- the number of parameters used to model distribu- vision of Wildlife and Ecology, Commonwealth Scientific and Industrial Research Organisation, Canberra. tions may be critical. Backhouse, D., Burgess, L.W., 1995. Mycogeography of Fusarium: (3) Similarly, as performance of different modeling climatic analysis of the distribution within Australia of Fusarium techniques may be differ across species, several species in section Gibbosum. Mycol. Res. 99, 1218–1224. models could be used (Thuiller, 2003). Backhouse, D., Burgess, L.W., 2002. Climatic analysis of the dis- (4) Confidence can be placed in the qualitative con- tribution of Fusarium graminearum, F-pseudograminearum and F-culmorum on cereals in Australia. Australas. Plant Pathol. 31, clusions of previous studies that have measured 321–327. distribution changes across multiple species, i.e. Baker, R.H.A., Sansford, C.E., Jarvis, C.H., Cannon, R.J.C., over this century many species may suffer reduc- MacLeod, A., Walters, K.F.A., 2000. The role of climatic map- tions in the amount of climatically suitable habitat ping in predicting the potential geographical distribution of available. non-indigenous pests under current and future climates. Agric. Ecosyst. Environ. 82, 57–71. (5) The number of parameters used to model distribu- Beaumont, L.J., Hughes, L., 2002. Potential changes in the distri- tions has less influence on distributions predicted butions of latitudinally restricted Australian butterfly species in under the worst-case climate change scenario than response to climate change. Global Change Biol. 8, 954–971. those predicted under the best-case scenario. This Bennett, S., Brereton, R., Mansergh, I., Berwick, S., Sandiford, K., suggests that as the intensity of warming increases, Wellington, C., 1991. The potential effect of the enhanced greenhouse climate change on selected Victorian fauna. Technical Re- real changes in response to warming outweigh pos- port Series No. 123. Department of Conservation and Environ- sible bias associated with over-fitting models. ment, Arthur Rylah Institute, Vic., Melbourne. 268 L.J. Beaumont et al. / Ecological Modelling 186 (2005) 250–269

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