Importance of interindividual interactions in eco‐evolutionary population dynamics: The rise of demo‐genetic agent‐based models. (27th November 2022)

• Record Type: Journal Article
• Authors: Lamarins, Amaïa; Fririon, Victor; Folio, Dorinda; Vernier, Camille; Daupagne, Léa; Labonne, Jacques; Buoro, Mathieu; Lefèvre, François; Piou, Cyril; Oddou‐Muratorio, Sylvie
• Is Part Of: Evolutionary applications. Volume 15:Number 12(2022)
• Page Start: 1988
• Page End: 2001
• Publication Date: 2022-11-27
• Subjects: agent‐based models -- demo‐genetic models -- DG‐ABMs -- eco‐evolutionary dynamics -- eco‐genetic models; Evolution (Biology) -- Periodicals; Genetics -- Periodicals; Natural selection -- Periodicals; Ecology -- Periodicals; 576.8
• DOI: 10.1111/eva.13508
• Languages: English
• ISSNs: 1752-4571

Abstract

Abstract: 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.