Mathieu Buoro

Investigating eco‐evolutionary responses of populations to environmental changes requires a solid understanding of the spatial context in which they evolve. While the interplay between local adaptation and dispersal in guiding evolutionary outcomes has been studied extensively, it is often in a context of divergent selection and simplified spatial structure. Alternatively, we used a spatially‐explicit demo‐genetic agent‐based model to simulate a complex network of interconnected populations of Atlantic salmon facing a perturbation shifting their genetic composition to create diversity among populations. Our model allowed us to track emerging demographic, phenotypic, and evolutionary changes from the individual to the metapopulation in a single, spatially realistic framework. We analyzed the influence of the spatial structure of genetic diversity and populations on the evolutionary dynamics under convergent selection (toward a common optimum). Our simulations showed adaptation and demographic recovery of local populations was enhanced by dispersal between initially diverse populations, providing general support for the adaptation network theory. This was particularly true for increased dispersal rates and a random spatial genetic structure. Importantly, our spatially realistic model emphasized that the evolutionary and demographic trajectories of local populations are context‐dependent and can be heavily influenced by the spatial configuration of populations linked by dispersal. Overall, the adaptive capacity of the network depended on the ‘opportunity for adaptation' provided by immigration patterns that emerged from the connectivity structures of the scenarios tested. We highlight the importance of spatial diversity and population structure for the ability of species to respond to environmental change, with implications for management and conservation of spatially structured populations.

The study of eco‐evolutionary dynamics, that is of the intertwinning between ecological and evolutionary processes when they occur at comparable time scales, is of growing interest in the current context of global change. However, many eco‐evolutionary studies overlook the role of interindividual interactions, which are hard to predict and yet central to selective values. Here, we aimed at putting forward models that simulate interindividual interactions in an eco‐evolutionary framework: the demo‐genetic agent‐based models (DG‐ABMs). Being demo‐genetic, DG‐ABMs consider the feedback loop between ecological and evolutionary processes. Being agent‐based, DG‐ABMs follow populations of interacting individuals with sets of traits that vary among the individuals. We argue that the ability of DG‐ABMs to take into account the genetic heterogeneity—that affects individual decisions/traits related to local and instantaneous conditions—differentiates them from analytical models, another type of model largely used by evolutionary biologists to investigate eco‐evolutionary feedback loops. Based on the review of studies employing DG‐ABMs and explicitly or implicitly accounting for competitive, cooperative or reproductive interactions, we illustrate that DG‐ABMs are particularly relevant for the exploration of fundamental, yet pressing, questions in evolutionary ecology across various levels of organization. By jointly modelling the effects of management practices and other eco‐evolutionary processes on interindividual interactions and population dynamics, DG‐ABMs are also effective prospective and decision support tools to evaluate the short‐ and long‐term evolutionary costs and benefits of management strategies and to assess potential trade‐offs. Finally, we provide a list of the recent practical advances of the ABM community that should facilitate the development of DG‐ABMs.