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

Abstract

The spatial configuration and size of patches influence metapopulation dynamics by altering colonisation–extinction dynamics and local density dependency. This spatial forcing as determined by the metapopulation typology then imposes strong selection pressures on life‐history traits, which will in turn feed back on the ecological metapopulation dynamics. 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 systems’ modelling approach to quantify the importance of spatial forcing and experimentally observed life‐history evolution for metapopulation demography as quantified by (meta)population size and variability. We therefore developed an individual‐based model matching an earlier experimental evolution with spider mites to perform virtual translocation and invasion experiments that would have been otherwise impossible to conduct. We show that (a) metapopulation demography is more affected by spatial forcing than by life‐history evolution, but that life‐history evolution contributes substantially to changes in local‐ and especially metapopulation‐level population sizes, (b) extinction rates are minimised by evolution in classical metapopulations, and (c) evolution is optimising individual performance in metapopulations when considering the importance of more cryptic stress resistance evolution. Ecological systems’ modelling opens up a promising avenue to quantify the importance of eco‐evolutionary feedbacks in spatially structured populations. Metapopulation sizes are especially impacted by evolution, but its variability is mainly determined by the spatial forcing. Eco‐evolutionary dynamics can increase the persistence of classical metapopulations. Conservation of genetic variation and, hence, adaptive potential is thus not only essential in the face of environmental change; it also generates putative rescuing feedbacks that impact metapopulation persistence.

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., ; Bonte & Bafort, ; De Meester et al., ; Fischer et al., ; Fronhofer & Altermatt, ; Huang et al., ; Litchman, Klausmeier, & Yoshiyama, ; Metcalf et al., ; Thomas, Kremer, Klausmeier, & Litchman, ; Van Nuland, Ware, Bailey, & Schweitzer, ) 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 , ).…”

Building a bridge between adaptive capacity and adaptive potential to understand responses to environmental change

“…Our review revealed an array of methods for estimating connectivity, including telemetry, movement ecology modeling, and gene flow estimation. Modeling approaches included systems modeling and dispersal kernels (Bonte & Bafort, 2019;Summers et al, 2012). Gene flow estimates are well established in the literature for understanding connectivity, and were measured through landscape genetics approaches, which have been applied to other conservation questions, such as restoration (Kaus et al, 2019;Manel et al, 2003;Proft et al, 2018).…”