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Brody Steven Sandel

Predictability in community dynamics

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  • Benjamin Blonder, Norwegian Univ Sci & Technol NTNU, Norwegian University of Science & Technology, Dept Biol, Environm Toxicol
  • ,
  • Derek E. Moulton, Univ Oxford, University of Oxford, Math Inst
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  • Jessica Blois, Univ Calif Merced, University of California Merced, University of California System, Sch Nat Sci
  • ,
  • Brian J. Enquist, Univ Arizona, University of Arizona, Dept Ecol & Evolutionary Biol
  • ,
  • Bente J. Graae, Norwegian Univ Sci & Technol NTNU, Norwegian University of Science & Technology, Dept Biol, Environm Toxicol
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  • Marc Macias-Fauria, Univ Oxford, University of Oxford, Sch Geog & Environm, Environm Change Inst
  • ,
  • Brian McGill, Univ Maine, University of Maine, University of Maine System, University of Maine Orono, Sch Biol & Ecol, Sustainabil Solut Initiat
  • ,
  • Sandra Nogue, Univ Southampton, University of Southampton, Dept Geog & Environm
  • ,
  • Alejandro Ordonez
  • Brody Sandel
  • Jens-Christian Svenning

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.

TidsskriftEcology Letters
Sider (fra-til)293-306
Antal sider14
StatusUdgivet - mar. 2017

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