OPTIMAL FORAGING AMONG COMPETITORS AND PREDATORS: A STATE-DEPENDENT MODEL INCORPORATING GAME THEORY

Abstract

Individuals must often make decisions regarding where and when to search for resources or prey, and when to behave aggressively if confronted by a competitor. Such decisions are likely to have important consequences for population dynamics and community dynamics. In this paper I focused on the situation where a forager feeds in the presence of predators and a competitor. I used stochastic dynamic programming and game theory to investigate how a forager could optimize its fitness if it had flexibility in when it chose to forage and exhibit aggression. I considered short-term forager fitness, defined in terms of daily food intake and survival. The investigation showed that increasing the risk of predation could modify optimal temporal patterns of foraging activity and aggression among competitors, even when predation risk was relatively low. Optimal foraging strategies were strongly related to the state of the forager (i.e., the amount of food it had consumed), the state of its competitors (i.e., the amount of food they had consumed), and the abundance of resources. In general, foragers were predicted to be more aggressive during confrontations over food as the risk of predation was increased. In addition, temporal partitioning was most likely to be an optimal strategy for subordinate individuals at intermediate levels of predation risk, and when food was abundant.