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Ecology Letters
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Predicting ecosystem stability from community composition and biodiversity

Authors: H. Wayne Polley; Alexandra Weigelt; Brian J. Wilsey; Forest Isbell; Forest Isbell; David Tilman; Enrica De Luca; +12 Authors

Predicting ecosystem stability from community composition and biodiversity

Abstract

AbstractAs biodiversity is declining at an unprecedented rate, an important current scientific challenge is to understand and predict the consequences of biodiversity loss. Here, we develop a theory that predicts the temporal variability of community biomass from the properties of individual component species in monoculture. Our theory shows that biodiversity stabilises ecosystems through three main mechanisms: (1) asynchrony in species’ responses to environmental fluctuations, (2) reduced demographic stochasticity due to overyielding in species mixtures and (3) reduced observation error (including spatial and sampling variability). Parameterised with empirical data from four long‐term grassland biodiversity experiments, our prediction explained 22–75% of the observed variability, and captured much of the effect of species richness. Richness stabilised communities mainly by increasing community biomass and reducing the strength of demographic stochasticity. Our approach calls for a re‐evaluation of the mechanisms explaining the effects of biodiversity on ecosystem stability.

Countries
United States, Netherlands, United States, Switzerland
Keywords

consequences, Ecology and Evolutionary Biology, Population Dynamics, population, Germany, Biomass, temporal stability, biodiversity, demographic stochasticity, Netherlands, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, competitive communities, species interactions, Institute of Evolutionary Biology and Environmental Studies, dynamics, Biodiversity, Texas, [SDV.EE] Life Sciences [q-bio]/Ecology, environment, Interdisciplinary Pilot Projects, 590 Animals (Zoology), Leerstoelgroep Natuurbeheer en plantenecologie, environmental stochasticity, 570, productivity, Minnesota, 577, Poaceae, Models, Biological, 333, diversity, Computer Simulation, Ecosystem, overyielding, Stochastic Processes, variability, time-series, prediction, stability, Models, Theoretical, 570 Life sciences; biology, SystemsX.ch

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    270
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
270
Top 1%
Top 1%
Top 1%
Green
bronze