globalchange  > 气候减缓与适应
DOI: 10.1111/ele.12736
Scopus记录号: 2-s2.0-85011332603
论文题名:
Predictability in community dynamics
作者: Blonder B.; Moulton D.E.; Blois J.; Enquist B.J.; Graae B.J.; Macias-Fauria M.; McGill B.; Nogué S.; Ordonez A.; Sandel B.; Svenning J.-C.
刊名: Ecology Letters
ISSN: 1461023X
EISSN: 1461-0248
出版年: 2017
卷: 20, 期:3
起始页码: 293
结束页码: 306
语种: 英语
英文关键词: Alternate states ; chaos ; climate change ; community assembly ; community climate ; community response diagram ; disequilibrium ; hysteresis ; lag ; memory effects
Scopus关键词: analytical method ; chaotic dynamics ; climate change ; community composition ; community response ; diagram ; disequilibrium ; ecophysiology ; hysteresis ; population distribution ; reconstruction ; biological model ; biota ; climate change ; ecology ; population dynamics ; procedures ; Biota ; Climate Change ; Ecology ; Models, Biological ; Population Dynamics
英文摘要: The coupling between community composition and climate change spans a gradient from no lags to strong lags. The no-lag hypothesis is the foundation of many ecophysiological models, correlative species distribution modelling and climate reconstruction approaches. Simple lag hypotheses have become prominent in disequilibrium ecology, proposing that communities track climate change following a fixed function or with a time delay. However, more complex dynamics are possible and may lead to memory effects and alternate unstable states. We develop graphical and analytic methods for assessing these scenarios and show that these dynamics can appear in even simple models. The overall implications are that (1) complex community dynamics may be common and (2) detailed knowledge of past climate change and community states will often be necessary yet sometimes insufficient to make predictions of a community's future state. © 2017 John Wiley & Sons Ltd/CNRS
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/107658
Appears in Collections:气候减缓与适应

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作者单位: Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom; Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway; Mathematical Institute, University of Oxford, Oxford, United Kingdom; School of Natural Sciences, University of California – Merced, Merced, CA, United States; Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, United States; School of Biology and Ecology, University of Maine, Orono, ME, United States; Department of Geography and Environment, University of Southampton, Southampton, United Kingdom; Section for Biodiversity & Ecoinformatics, Department of Bioscience, Aarhus University, Aarhus C, Denmark; School of Geography and the Environment, University of Oxford, Oxford, United Kingdom

Recommended Citation:
Blonder B.,Moulton D.E.,Blois J.,et al. Predictability in community dynamics[J]. Ecology Letters,2017-01-01,20(3)
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