Evolution of informed dispersal strategies in trophic meta-communities

Abstract

Living organisms move and their movement is both a response to local conditions and, often, the cause of change in those conditions. This feedback loop is often overlooked in theoretical studies on the evolution of dispersal, which assume that either the decision to leave is uninformed or that the local conditions are exogenously driven. Here, we embrace the feedback loop and study what dispersal strategies evolve in a trophic meta-population where the dynamics are entirely endogenous. We show that there are five possible classes of strategies that can evolve depending on the ecological conditions: leaving once local conditions fall low enough (Unsaturated), having an extended stay even under adverse conditions (Saturated), leaving from a high quality location (Anxious), leaving independently of the location state (Ignorant), and completely abstaining from dispersal (No-dispersal). The Unsaturated class captures the classical prediction of the marginal value theorem, while the other four extend the range of possible evolutionarily optimal strategies. Which class of strategies evolves depends on the kind of information being sensed (resource availability versus conspecifics density), the size of the local consumer population at equilibrium, and the stability of this equilibrium. Our results provide a theoretical underpinning for the diversity of movement strategies observed in nature that deviate from classic predictions and suggest a comparative framework that can inform experimental design.