The importance and adaptive value of life history evolution for metapopulation dynamics

Abstract

Abstract:The performance of populations is affected by environmental change and the resulting evolutionary dynamics. The spatial configuration and size of patches is known to directly influence metapopulation dynamics (spatial forcing). These metapopulation dynamics are also affecting and affected by life history evolution. Given the relevance of metapopulation persistence for biological conservation, and the potential rescuing role of evolution, a firm understanding of the relevance of these eco-evolutionary processes is essential.We here follow a system's modelling approach to disentangle the role of metapopulation structure relative to evolution for metapopulation performance. We developed an individual based systems model that is strongly based and parameterized by results from experimental metapopulations with spider mites. This model enables us to perform virtual translocation and invasion experiments that would have been impossible to conduct in our experimental systems.We show that (1) metapopulation demography is more affected by spatial forcing than by observed life history evolution, but that life history evolution contributes up to 20% of the variation in demographic measures related to spatiotemporal variance in population sizes, (2) metapopulation performance is not enhanced by evolution, and (3) evolution is optimising individual performance in metapopulations when considering the importance of so far overlooked stress resistance evolution.We thus provide evidence that metapopulation-level selection maximises individual performance and more importantly, that -at least in our system-evolutionary changes impact metapopulation dynamics, especially factors related to local and metapopulation sizes.

Eco‐evolutionary feedbacks—Theoretical models and perspectives

“…This experiment confirmed the antiphase oscillations predicted from theory when prey evolves defence strategies. While such prominent examples of the integration of theory and empirical data on EEFs exist (see among others also, Becks et al, 2012;Bonte & Bafort, 2018;De Meester et al, 2019;Fischer et al, 2014;Fronhofer & Altermatt, 2015;Huang et al, 2017;Litchman, Klausmeier, & Yoshiyama, 2009;Metcalf et al, 2008;Thomas, Kremer, Klausmeier, & Litchman, 2012;Van Nuland, Ware, Bailey, & Schweitzer, 2019) breadth of the theoretical work highlighted here, the coupling of empirical data from natural and experimental settings, with theoretical models, needs to be deepened. This gap between theory and empirical work may, in part, be due to differences in technical jargon that impede effective communication between theoreticians and empiricists as well as modelling specializations among theoreticians which impede synthesis or at least slowdown progress.…”

Spatial connectedness imposes local‐ and metapopulation‐level selection on life history through feedbacks on demography

“…Changes in fluxes among patches are known to directly impact metapopulation and local population dynamics (Cheptou et al , ; Bonte et al , ). These demographic changes result from direct changes in the spatial structure of the network, for instance by changes in connectedness, patch size or the presence of external extinctions or from trait evolution (De Roissart et al , ; Bonte & Bafort, ). Moreover, spatial networks are usually heterogeneous and modular with respect to their topology (Fahrig, ; Urban & Skelly, ; Ferrari et al , ).…”

Individual heterogeneity and its importance for metapopulation dynamics

“…Contrasting the model outcomes to the evolved dispersal rates across the experimental replicates indeed shows that data and model outcomes correlate well, independently from dispersal mortality ( fig.1). As multiple traits evolve when connectedness is changing, including some at the local level [27], [29], they may balance each other and erode systematic demographic changes across the connectedness treatments. When kin and kind structure is removed by reshuffling, the least connected metapopulations show higher levels of variability.…”