Fitting Dynamic Models to Animal Movement Data: The Importance of Probes for Model Selection, a Reply to Franz and Caillaud

Abstract

Analyzing animal movement can provide a useful perspective on the interface between landscape patterns and individual behavior (Patterson et al. 2008; Schick et al. 2008). In Dalziel et al. (2008) we proposed a method of fitting dynamic movement models to trajectory and landscape data. The goal was to explore how disparate landscape-behavior processes combine to generate animal movement patterns. As an example of the approach, we used a genetic algorithm (GA) to fit artificial neural network (ANN) models to observations of elk (Cervus canadensis) movement. We formulated seven ANN models that encompassed a factorial combination of three different types of landscapebehavior interaction: the distance from an elk’s current location to each point on the landscape, d(x); the resource structure at that point relative to the rest of the home range, r(x); and the estimated memory of previous visits to that point, m(x), where x represents the coordinates of the landscape. The models used this information to estimate a dynamic redistribution kernel that, at each time point, gave the probability that an elk would move to a given location x ∗ at the next time step. Thus, ∗ P(x p x) p k[d(x), r(x), m(x)],