Importance of interindividual interactions in eco‐evolutionary population dynamics: The rise of demo‐genetic agent‐based models

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.

CDMetaPOP 2: a multispecies, eco‐evolutionary simulation framework for landscape demogenetics and connectivity

“…The incorporation of multispecies interactions within individual-based, eco-evolutionary simulation frameworks is not novel. Most previous applications have been system-specific or focused on macroevolution (Lamarins et al 2022), whereas CDMetaPOP's strength lies in generalizability across species and systems with a strong focus on ecological and behavioral processes. Slim 4 (Haller and Messer 2023) is a generalizable model that also recently added multispecies interactions to its eco-evolutionary simulation framework.…”

“…When paired with simulation modeling, a multispecies framework can be used to explore questions that might otherwise be constrained to single species, single landscapes, or limited timeframes. Simulations that are individual‐based (or agent‐based) provide a powerful vehicle for exploring eco‐evolutionary processes by simulating heterogeneity in individual characteristics and genotypes both within and between species (Lamarins et al 2022). That heterogeneity can drive factors such as individual fitness or reproduction, and scales up to produce evolutionary patterns at the population level.…”

“…While species interactions occur in a variety of ways (e.g. predator–prey, parasitism), we first implemented competition not only because it is the most commonly cited interaction in the literature (Lamarins et al 2022), but because competition is the interaction for which user demand has been the highest. The modeling framework presented here provides the opportunity to ask broad questions about the role of functional landscape connectivity in conservation planning for communities and the effects of species interactions on eco‐evolutionary processes such as gene flow, adaptation, dispersal, invasion, extinction, and range expansion.…”

The importance of network spatial structure as a driver of eco‐evolutionary dynamics

“…Importantly, DG-ABMs do not assume an a priori fitness function. Instead, variation in fitness emerges from eco-evolutionary processes, individual decisions, interactions and feedbacks, resulting in the evolution of patterns structuring genetic diversity and population dynamics (Lamarins et al 2022a). By also allowing the explicit representation of the spatial structure of populations, these models facilitate the emergence of unanticipated eco-evolutionary feedbacks (Travis and Dytham 1998).…”

Corrigendum

“…In Evolutionary Applications 15:12, for the article by Lamarins et al ( 2022 ) entitled “Importance of interindividual interactions in eco‐evolutionary population dynamics: The rise of demo‐genetic agent‐based models” (pages 1988–2001), the authors would like to include an Author Contributions section as follows:…”