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Per capita interactions and stress tolerance drive stress-induced changes in biodiversity effects on ecosystem functions

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  • Jan M. Baert

    (Laboratory of Environmental Toxicology and Applied Ecology, Ghent University)

  • Colin R. Janssen

    (Laboratory of Environmental Toxicology and Applied Ecology, Ghent University)

  • Koen Sabbe

    (Laboratory of Protistology and Aquatic Ecology, Ghent University)

  • Frederik De Laender

    (Research Unit of Environmental and Evolutionary Biology, University of Namur)

Abstract

Environmental stress changes the relationship between biodiversity and ecosystem functions, but the underlying mechanisms are poorly understood. Because species interactions shape biodiversity–ecosystem functioning relationships, changes in per capita interactions under stress (as predicted by the stress gradient hypothesis) can be an important driver of stress-induced changes in these relationships. To test this hypothesis, we measure productivity in microalgae communities along a diversity and herbicide gradient. On the basis of additive partitioning and a mechanistic community model, we demonstrate that changes in per capita interactions do not explain effects of herbicide stress on the biodiversity–productivity relationship. Instead, assuming that the per capita interactions remain unaffected by stress, causing species densities to only change through differences in stress tolerance, suffices to predict the stress-induced changes in the biodiversity–productivity relationship and community composition. We discuss how our findings set the stage for developing theory on how environmental stress changes biodiversity effects on ecosystem functions.

Suggested Citation

  • Jan M. Baert & Colin R. Janssen & Koen Sabbe & Frederik De Laender, 2016. "Per capita interactions and stress tolerance drive stress-induced changes in biodiversity effects on ecosystem functions," Nature Communications, Nature, vol. 7(1), pages 1-8, November.
  • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms12486
    DOI: 10.1038/ncomms12486
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    Cited by:

    1. Gobeyn, Sacha & Mouton, Ans M. & Cord, Anna F. & Kaim, Andrea & Volk, Martin & Goethals, Peter L.M., 2019. "Evolutionary algorithms for species distribution modelling: A review in the context of machine learning," Ecological Modelling, Elsevier, vol. 392(C), pages 179-195.

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