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Quantifying eco-evolutionary contributions to trait divergence in spatially structured systems

Cite this dataset

Govaert, Lynn; Pantel, Jelena H; De Meester, Luc (2022). Quantifying eco-evolutionary contributions to trait divergence in spatially structured systems [Dataset]. Dryad.


Ecological and evolutionary processes can occur at similar time scales, and hence influence one another. There has been much progress in developing metrics that quantify contributions of ecological and evolutionary components to trait change over time. However, many empirical evolutionary ecology studies document trait differentiation among populations structured in space. In both time and space, the observed differentiation in trait values among populations and communities can be the result of interactions between non-evolutionary (phenotypic plasticity, changes in the relative abundance of species) and evolutionary (genetic differentiation among populations) processes. However, the tools developed so far to quantify ecological and evolutionary contributions to trait changes are implicitly addressing temporal dynamics because they require directionality of change from an ancestral to a derived state. Identifying directionality from one site to another in spatial studies of eco-evolutionary dynamics is not always possible and often not meaningful. We suggest three modifications to existing partitioning metrics so they allow quantifying ecological and evolutionary contributions to changes in population and community trait values across spatial locations in landscapes. Applying these spatially modified metrics to published empirical examples shows how these metrics can be used to generate new empirical insights and to facilitate future comparative analyses. The possibility to apply eco-evolutionary partitioning metrics to populations and communities in natural landscapes is critical as it will broaden our capacity to quantify eco-evolutionary interactions as they occur in nature.


This data has been collected by previous studies, with citations provided in main text of manuscript. R code and the specific data used in this study are available here.


KU Leuven Research Fund, Award: C16/2017/002

Research Foundation - Flanders, Award: G0B9818

Richard Lounsbery Foundation

Agentschap voor Innovatie door Wetenschap en Technologie

University of Zurich