The role of context in elucidating drivers of animal movement

Despite its consequences for ecological processes and population dynamics, intra‐specific variability is frequently overlooked in animal movement studies. Consequently, the necessary resolution to reveal drivers of individual movement decisions is often lost as animal movement data are aggregated to infer average or population patterns. Thus, an empirical understanding of why a given movement pattern occurs remains patchy for many taxa, especially in marine systems. Nonetheless, movement is often rationalized as being driven by basic life history requirements, such as acquiring energy (feeding), reproduction, predator‐avoidance, and remaining in suitable environmental conditions. However, these life history requirements are central to every individual within a species and thus do not sufficiently account for the high intra‐specific variability in movement behavior and hence fail to fully explain the occurrence of multiple movement strategies within a species. Animal movement appears highly context dependent as, for example, within the same location, the behavior of both resident and migratory individuals is driven by life history requirements, such as feeding or reproduction, however different movement strategies are utilized to fulfill them. A systematic taxa‐wide approach that, instead of averaging population patterns, incorporates and utilizes intra‐specific variability to enable predictions as to which movement patterns can be expected under a certain context, is needed. Here, we use intra‐specific variability in elasmobranchs as a case study to introduce a stepwise approach for studying animal movement drivers that is based on a context‐dependence framework. We examine relevant literature to illustrate how this context‐focused approach can aid in reliably identifying drivers of a specific movement pattern. Ultimately, incorporating behavioral variability in the study of movement drivers can assist in making predictions about behavioral responses to environmental change, overcoming tagging biases, and establishing more efficient conservation measures.