Accounting for spatiotemporal patterns of long‐term recursion in estimating local‐scale step selection

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Abstract Step‐selection analysis (SSA) is ubiquitous for assessing local‐scale habitat selection by comparing relocations from telemetry data (used steps) to potential relocations given the movement of the animal (available steps). The case–control design of SSA is intended to estimate selection at the spatiotemporal scale of these steps. However, long‐term behaviour associated with recurring use of certain locations or resources may additionally impact movement decisions, potentially impacting the estimates produced by SSA. To determine the impact of recursive behaviour on local selection, we simulated movement trajectories in which animals exhibited patterns of recursion. Based on these simulated trajectories, we evaluated the impact of long‐term behaviour on estimates of step‐selection. Then, we developed a new approach to identify recursion points based on a time‐dependent kernel density estimate including latitude, longitude and time of day. Based on this information, we accounted for recursive behaviour by including the relationship between available steps to recursion points or using recursion points to adjust our sample of available steps. Finally, we apply this approach to estimate the response of white‐tailed deer (Odocoileus virginianus) to sources of risk. Recursive movement resulted in biased estimates of selection when using previously established step‐selection methods. However, we found that our correction models were able to produce accurate estimates across differing recursion scenarios. When applied to an empirical deer data set, these methods revealed a pattern of selection and avoidance for human modified areas that was masked by previous approaches. Our results suggest that long‐term recursive behaviour may have unappreciated effects on estimates from SSA. Specifically, since the used and available steps in SSA are associated with specific places and times, covariates must account for variation in spatial and temporal patterns of long‐term behaviour. Unless properly accounted for, the effects of long‐term recursive behaviour will inhibit the ability of SSA to characterize fine‐scale behaviours such as predator response or foraging ecology.