Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use

Abstract

Abstract The ability to predict animal space use patterns is a fundamental concern in changing environments. Such predictions require a detailed understanding of the movement mechanisms from which spatial distributions emerge. However, these are typically complex, multifaceted, and therefore difficult to uncover. Here, we provide a methodological framework for uncovering the movement mechanisms necessary for building predictive models of animal space use. Our procedure begins by parametrising a movement model of each individual in a population using step selection analysis, from which we build an individual‐based model (IBM) of interacting individuals, derive predicted broad‐scale space use patterns from the IBM and then compare the predicted and empirical patterns. Importantly, discrepancies between these predicted and empirical patterns are used to formulate new hypotheses about the drivers of animal movement decisions and thus iteratively improve the model's predictive power. We demonstrate our method on a population of feral pigs in Mississippi, USA. Our technique incorporates both social interactions between individuals and environmental drivers of movement. At each iteration of model construction, we were able to identify missing features to improve model prediction by analysing the IBM output. These include overuse‐avoidance effects of self‐attractive mechanisms (i.e. attraction to previously visited sites becomes repulsion if there have been multiple visits in quick succession), which were vital for ensuring predicted occurrence distributions do not become vanishingly small. Overall, we have provided a general method for iteratively improving the predictive power of step selection models. This will enable future researchers to maximise the information obtained from step selection analyses and to highlight potentially missing data for uncovering the drivers of movement decisions and emergent space use patterns. Ultimately, this provides a fundamental step towards the general aim of constructing predictive models of animal space use.