Achievement

Organisms searching for resources in environments with rare, noisy and non-directional information are often modeled as random walks. Our biology fellow, Andrew Hein, along with his interdisciplinary faculty advisor in math, Scott McKinley, published work in the Proceedings of the National Academy of Sciences of the U.S.A. that extended the classical model to include searcher responses to noisy sensory data. They explored the consequences of searcher responses using simulations of a visual-olfactory predator in search of prey. Their results indicated that incorporating even simple predator responses into the model dominated other features of a random search resulting in lower mean search times and decreased risk of long intervals between target encounters. Searchers quickly abandoned regions with low densities of prey and concentrated in regions near prey. The resulting area-restricted search behavior is similar to that observed of many organisms in nature.