Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia

NJ Briscoe; MR Kearney; CA Taylor; BA Wintle

Journal article

Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia

NJ Briscoe, MR Kearney, CA Taylor, BA Wintle

Global Change Biology | Published : 2016

DOI: 10.1111/gcb.13280

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Abstract

Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long-term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the qualit..

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University of Melbourne Researchers

Natalie Briscoe Author

Michael Kearney Author

Brendan Wintle Author

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Grants

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Funding Acknowledgements

Data custodians for additional koala records: Victorian Department of Environment and Primary Industries, Queensland Department of Environment and Heritage Protection, and NSW Office of Environment and Heritage. We thank the Victorian Life Sciences Initiative (VLSCI) for providing access to high-performance computing facilities essential to this study. We also thank Warren Porter, Kath Handasyde and Andrew Krockenberger for their help developing the Niche Mapper koala model, Jane Elith for advice on Maxent, David Karoly for advice on generating future daily climate layers, and our editor and three anonymous referees for helpful comments on the manuscript. NJB was supported by NERP Environmental Decisions Hub, MRK was supported by an Australian Research Council grant (DP110102813), BAW was supported by an ARC Future Fellowship (FT100100819).