A review of statistical models used to characterize species-habitat associations with animal movement data

Understanding species-habitat associations is fundamental to ecological sciences and for species conservation. Consequently, various statistical approaches have been designed to infer species-habitat associations. Due to their conceptual and mathematical differences, these methods can yield contrasting results. In this paper, we describe and compare commonly used statistical models that relate animal movement data to environmental data. Specifically, we examined selection functions which include resource selection function (RSF) and step-selection function (SSF), as well as hidden Markov models (HMMs) and related methods such as state-space models. We demonstrate differences in assumptions of each method while highlighting advantages and limitations. Additionally, we provide guidance on selecting the most appropriate statistical method based on research objectives and intended inference. To demonstrate the varying ecological insights derived from each statistical model, we apply them to the movement track of a single ringed seal in a case study. For example, the RSF indicated selection of areas with high prey diversity, whereas the SSFs indicated no discernable relationship with prey diversity. Furthermore, the HMM reveals variable associations with prey diversity across different behaviors. Notably, the three models identified different important areas. This case study highlights the critical significance of selecting the appropriate model to identify species-habitat relationships and specific areas of importance. Our comprehensive review provides the foundational information required for making informed decisions when choosing the most suitable statistical methods to address specific questions, such as identifying expansive corridors or protected zones, understanding movement patterns, or studying behaviours.