An introduction to statistical models used to characterize species-habitat associations with animal movement data

Abstract

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. We describe and compare commonly used statistical models that relate animal movement data to environmental data, including resource selection functions (RSF), step-selection functions (SSF), and hidden Markov models (HMMs). We demonstrate differences in assumptions and highlighting advantages and limitations of each method. Additionally, we provide guidance on selecting the most appropriate statistical method based on the scale of the data and intended inference. To illustrate the varying ecological insights derived from each model, we apply them to the movement track of a single ringed seal in a case study. We demonstrate that each model yields varying ecological insights. For example, while the selection coefficient values from RSFs appear to show a stronger positive relationship with prey diversity than those of the SSFs, when we accounted for the autocorrelation in the data none of these relationships with prey diversity were statistically significant. 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 as an essential step in the process of identifying species-habitat relationships and specific areas of importance. Our review provides the foundational information required for making informed decisions when choosing the most suitable statistical methods to address specific questions, such as identifying protected zones, understanding movement patterns, or studying behaviours.