globalchange  > 影响、适应和脆弱性
DOI: 10.1111/gcb.13280
论文题名:
Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia
作者: Briscoe N.J.; Kearney M.R.; Taylor C.A.; Wintle B.A.
刊名: Global Change Biology
ISSN: 13541013
出版年: 2016
卷: 22, 期:7
起始页码: 2425
结束页码: 2439
语种: 英语
英文关键词: biophysical models ; climate change ; koala ; Phascolarctos cinereus ; refugia ; species distribution models
Scopus关键词: bioenergetics ; climate change ; climate prediction ; correlation ; ecological impact ; environmental conditions ; mammal ; refugium ; species-area relationship ; Animalia ; Phascolarctidae ; Phascolarctos cinereus
英文摘要: 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 quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat-induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long-term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range – with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot-spells, in driving species–climate relationships and distributions. By unpacking the mechanisms captured by correlative SDMs, we can increase our certainty in forecasts of climate change impacts on species. © 2016 John Wiley & Sons Ltd
资助项目: 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).
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/61349
Appears in Collections:影响、适应和脆弱性

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作者单位: School of BioSciences, University of Melbourne, Melbourne, VIC, Australia; Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC, Australia

Recommended Citation:
Briscoe N.J.,Kearney M.R.,Taylor C.A.,et al. Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia[J]. Global Change Biology,2016-01-01,22(7)
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