Evolution of innate behavioral strategies through competitive population dynamics

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.Author summary: The innate, or instinctual, behavioral strategies that populations of organisms employ to navigate their environments and fend for survival are shaped over epochs of evolutionary selection, in contrast to individual behaviors that can change within an individual’s lifetime based on experience and sensory input. Understanding how the interplay between organism and their environment shapes which behavior strategies emerge as the most successful for a population’s survival is a major problem in mathematical biology. Often, evolution is modeled as an optimization process that selects for behaviors that optimize the “fitness” of organisms in their environment. However, the fundamental evolutionary events are stochastic birth and death events, and the most successful organisms that emerge under these dynamics are not always those predicted by fitness-based approaches. In this work, we use agent-based stochastic simulations and mean-field approximations of a mechanistic population dynamics model to investigate the evolution of a population’s innate foraging strategies. In particular, we investigate when an emergent fitness function can be derived and how competition between individuals for resources alters the most successful behavioral strategies and precludes the derivation of a simple fitness function that predicts the most successful behavioral strategies.