RANDOM SEARCH MODELS OF FORAGING BEHAVIOR: THEORY, SIMULATION, AND OBSERVATION

Many organisms, from bacteria to primates, use stochastic movement patterns to find food. These movement patterns, known as search strategies, have recently be-come a focus of ecologists interested in identifying universal properties of optimal foraging behavior. In this dissertation, I describe three contributions to this field. First, I propose a way to extend Charnov’s Marginal Value Theorem to the spatially explicit framework of stochastic search strategies. Next, I describe simulations that compare the efficiencies of sensory and memory-based composite search strategies, which involve switching between different behavioral modes. Finally, I explain a new behavioral analysis protocol for identifying the factors that influence pollinator for-aging. The utility of this protocol is demonstrated using data gathered on sweat bees (Agapostemon) in Western Nebraska. ii