Use of Climatic Parameters in BIOCLIM and Its Impact on Predictions of Species’ Current and Future Distributions
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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). 0304-3800/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi: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 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.