Broadly speaking, the goal of movement ecology is to enrich our understanding of population dynamics by illuminating its dependence on movement patterns and the interaction of a given animal species with its environment. Thus, underlying every question about animal movement is a presumed implication for consequent success in finding food, shelter and mates while evading threats. The relationship between foraging efficiency and population dynamics, for example, has been studied since at least the 1920s. In looking at many of these long-standing mathematical models, one notices that they lean heavily on assumptions that have their roots in chemical kinetic theory and ideal gas laws. Plainly though, a grizzly bear does not forage for food in the same way an oxygen molecule careens around your living room!
In fairness many of these classical models do a marvelous job in explicating fundamental ecological principles. But the flood of recent GPS and other animal tracking data stimulates new questions and demands new approaches. I am interested in developing analytically tractable mathematical models that capture animal movement dynamics that are missed by the classical theory. As an example, my recent graduate student Andrew Hein and I have been developing a theoretical framework to study how searchers can utilize sparse, noisy signals that do not contain directional information. To our surprise, making a few subtle changes to the rules of how a predator conducts search led to a non-traditional functional form for the encounter rate between species.
Andrew M. Hein, Scott A. McKinley. “Sensory Information and Encounter Rates of Interacting Species.” PLoS Computational Biology 9(8): e1003178. 2013.
Elizabeth A Hamman, Craig W Osenberg, Scott A McKinley, and Adrian C Stier. “Spatial patterns of symbionts arising from propagule redirection” Submitted. (2015)