globalchange  > 影响、适应和脆弱性
DOI: 10.1111/gcb.13251
论文题名:
Benchmarking novel approaches for modelling species range dynamics
作者: Zurell D.; Thuiller W.; Pagel J.; Cabral J.S.; Münkemüller T.; Gravel D.; Dullinger S.; Normand S.; Schiffers K.H.; Moore K.A.; Zimmermann N.E.
刊名: Global change biology
ISSN: 13652486
出版年: 2016
卷: 22, 期:8
起始页码: 2651
结束页码: 2664
语种: 英语
英文关键词: climate change ; demographic models ; dispersal ; population viability ; prediction ; simulated data ; species distribution models ; virtual ecologist approach
Scopus关键词: benchmarking ; biodiversity ; climate change ; demography ; dispersal ; population viability analysis ; prediction ; Bayes theorem ; benchmarking ; biological model ; climate ; climate change ; ecosystem ; population dynamics ; Bayes Theorem ; Benchmarking ; Climate ; Climate Change ; Ecosystem ; Models, Biological ; Population Dynamics
英文摘要: Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. © 2016 John Wiley & Sons Ltd.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/61347
Appears in Collections:影响、适应和脆弱性

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作者单位: Dynamic Macroecology, Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903, Birmensdorf, Switzerland; Univ. Grenoble Alpes, Laboratoire d'Écologie Alpine (LECA), UMR-CNRS 5553 Université J. Fourier BP 53, F-38000, Grenoble, France; CNRS, Laboratoire d'Écologie Alpine (LECA), UMR-CNRS 5553 Université J. Fourier BP 53, F-38000, Grenoble, France; Institute of Landscape and Plant Ecology, University of Hohenheim, August-v.Hartmann-Str. 3, D-70599, Stuttgart, Germany; Biodiversity, Macroecology and Conservation Biogeography, University Göttingen, Büsgenweg 2, D-37077, Goettingen, Germany; Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, D-04103, Leipzig, Germany; Université de Québec à Rimouski, 300 Allée des Ursulines, Rimouski, Canada, G5L 3A1; Department of Botany and Biodiversity Research, University of Vienna, Rennweg 14, 1030, Vienna, Austria; Section for Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000, Aarhus C, Denmark; Senckenberg Biodiversity and Climate Research Centre (BiK-F), Georg Voigt-Straße 14-16, D-60325, Frankfurt (Main), Germany; Center for Population Biology, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA; Department of Environmental Systems Science, Swiss Federal Institute of Technology ETH, CH-8092, Zurich, Switzerland

Recommended Citation:
Zurell D.,Thuiller W.,Pagel J.,et al. Benchmarking novel approaches for modelling species range dynamics[J]. Global change biology,2016-01-01,22(8)
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