Evolution of innate behavioral strategies through competitive population dynamics

Abstract

Many organism behaviors are innate or instinctual and have been “hard-coded” through evolution. Current approaches to understanding these behaviors model evolution as an optimization problem in which the traits of organisms are assumed to optimize an objective function representing evolutionary fitness. Here, we use a mechanistic birth-death dynamics approach to study the evolution of innate behavioral strategies in a simulated population of organisms. In particular, we performed agent-based stochastic simulations and mean-field analyses of organisms exploring random environments and competing with each other to find locations with plentiful resources. We find that when organism density is low, the mean-field model allows us to derive an effective objective function, predicting how the most competitive phenotypes depend on the exploration-exploitation trade-off between the scarcity of high-resource sites and the increase in birth rate those sites offer organisms. However, increasing organism density alters the most competitive behavioral strategies and precludes the derivation of a well-defined objective function. Moreover, there exists a range of densities for which the coexistence of many phenotypes persists for evolutionarily long times.

Emergence of multiple foraging strategies under competition

“…Our model does not assume that the population optimizes a fitness function. Stochastic models, including agent-based models and birth-death processes, have been used to predict the impact of intra- and interspecific competition on the evolution of foraging strategies [Doebeli et al, 2017; Liang and Brinkman, 2022]. While deterministic models can also be used to predict how a population will evolve under intra- and interspecific competition [Chesson, 2000], they necessitate modeling the impact of competition, which is often mathematically intractable.…”

“…Our results are based on the analysis, and stochastic simulations of a Markovian agent-based model that captures some of the fundamental processes that drive the evolution of foraging strategies [Gillespie, 1976;Champagnat et al, 2006Champagnat et al, , 2008Doebeli et al, 2017;Liang and Brinkman, 2022]. The birth and death rates in our model depend indirectly on the success of the foraging strategy of an individual, and, through competition, on the foraging strategies of other individuals in the population.…”