Quantifying eco‐evolutionary contributions to trait divergence in spatially structured systems. Issue 4 (28th June 2022)

• Record Type: Journal Article
• Authors: Govaert, Lynn; Pantel, Jelena H.; De Meester, Luc
• Is Part Of: Ecological monographs. Volume 92:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-28
• Subjects: community trait change -- eco‐evolutionary dynamics -- partitioning metrics -- population trait change -- spatial trait variation; Ecology -- Periodicals; Ecology; Écologie; Electronic journals; Periodicals; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 577
• DOI: 10.1002/ecm.1531
• Languages: English
• ISSNs: 0012-9615

Abstract

Abstract: 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 nonevolutionary (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 of applying 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.