Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/36938
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- Title
- Predicting species distributions : use of climatic parameters in BIOCLIM and its impact on predictions of species' current and future distributions
- Related
- Ecological modelling, Vol. 186, Issue 2, p.250-269
- DOI
- 10.1016/j.ecolmodel.2005.01.030
- Publisher
- Elsevier Science
- Date
- 2005
- Author/Creator
- Beaumont, Linda J
- Author/Creator
- Hughes, Lesley
- Author/Creator
- Poulsen, Michael
- Description
- 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. 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.
- Description
- 20 page(s)
- Subject Keyword
- BIOCLIM
- Subject Keyword
- bioclimatic envelope
- Subject Keyword
- butterflies
- Subject Keyword
- climate change
- Subject Keyword
- predictive modeling
- Subject Keyword
- range shifts
- Resource Type
- journal article
- Organisation
- Macquarie University. Dept. of Biological Sciences
- Organisation
- Macquarie University. Dept. of Human Geography
- Identifier
- http://hdl.handle.net/1959.14/36938
- Identifier
- ISSN:0304-3800
- Identifier
- mq-rm-2005001004
- Language
- eng
- Reviewed
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