Background: New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods.
Methods: We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared the performances of spatial logistic regression models (SLRMs), point process models (PPMs), and integrated step selection models ((i)SSMs) and their interplays with habitat, tracking-device, and animal movement properties.
Results: We demonstrated that SLRMs were inappropriate for large-scale attraction studies and prone to bias when inferring habitat selection. In contrast, PPMs and (i)SSMs showed comparable (unbiased) performances for both habitat selection and large-scale effect studies. However, (i)SSMs had several advantages over PPMs with respect to robustness, user-friendly implementation, and computation time.
Conclusions: We recommend the use of (i)SSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. This method has several practical advantages over PPMs and additionally extends SSMs, thus increasing its predictive capacity and allowing the derivation of mechanistic movement models.