1. Animal movement is often modeled in discrete time, formulated in terms of steps taken between successive locations at regular time intervals. Steps are characterized by the distance between successive locations (step-lengths) and changes in direction (turn angles). Animals commonly exhibit a mix of directed movements with large step lengths and turn angles near 0 when traveling between habitat patches and more wandering movements with small step lengths and uniform turn angles when foraging. Thus, step-lengths and turn angles will typically be cross-correlated.
2. Most models of animal movement assume that step-lengths and turn angles are independent, likely due to a lack of available alternatives. Here, we show how the method of copulae can be used to fit multivariate distributions that allow for correlated step lengths and turn angles.
3. We describe several newly developed copulae appropriate for modeling animal movement data and fit these distributions to data collected on fishers (Pekania pennanti). The copulae are able to capture the inherent correlation in the data and provide a better fit than a model that assumes independence. Further, we demonstrate via simulation that this correlation can impact movement patterns (e.g. rates of dispersion overtime).
4. We see many opportunities to extend this framework (e.g. to consider autocorrelation in step attributes) and to integrate it into existing frameworks for modeling animal movement and habitat selection. For example, copula could be used to more accurately sample available locations when conducting habitat-selection analyses.
One Small Step for PMDD, One Large Step for Affective Disorders
