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Measuring the contribution of evolution to community trait structure in freshwater zooplankton

Citation

Govaert, Lynn et al. (2021), Measuring the contribution of evolution to community trait structure in freshwater zooplankton, Dryad, Dataset, https://doi.org/10.5061/dryad.rxwdbrv8s

Abstract

There are currently few predictions about when evolutionary processes are likely to play an important role in structuring community features.Determining predictors that indicate when evolution is expected to impact ecological processes in natural landscapes can help researchers identify eco-evolutionary 'hotspots', where eco-evolutionary interactions are more likely to occur. Using data collected from a survey in freshwater cladoceran communities, landscape population genetic data, and phenotypic trait data measured in a common garden, we applied a Bayesian linear model to assess whether the impact of local trait evolution in the keystone species Daphnia magna on cladoceran community trait values could be predicted by population genetic properties (within-population genetic diversity, genetic distance among populations), ecological properties (Simpson’s diversity, phenotypic divergence), or environmental divergence. We found that the impact of local trait evolution varied among communities. Moreover, community diversity and phenotypic divergence were found to be better predictors of the contribution of evolution to community trait values than environmental features or genetic properties of the evolving species. Our results thus indicate the importance of ecological context for the impact of evolution on community features. Our study also demonstrates one way to detect signatures of eco-evolutionary interactions in communities inhabiting heterogeneous landscapes using survey data of contemporary ecological and evolutionary structure.

Funding

KU Leuven Research Council, Award: PF/2010/07

KU Leuven Research Council, Award: C16/17/002

Fonds Wetenschappelijk Onderzoek, Award: G0B9818

Fonds Wetenschappelijk Onderzoek, Award: G0C3818

KU Leuven Research Fund, Award: F+ 13036

Agentschap voor Innovatie door Wetenschap en Technologie, Award: PhD fellowship

KU Leuven Research Council, Award: PF/2010/07

KU Leuven Research Fund, Award: F+ 13036