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.

翻译：理解物种-栖息地关联性是生态科学和物种保护的基础。因此，学界设计了多种统计方法来推断物种-栖息地关联。由于这些方法在概念和数学上的差异，它们可能产生相互矛盾的结果。本文系统描述并比较了常用于关联动物运动数据与环境数据的统计模型，包括资源选择函数（RSF）、步骤选择函数（SSF）和隐马尔可夫模型（HMM）。我们阐释了各方法的假设差异，并指出其优势与局限性。此外，我们根据数据尺度与推断目标提供了选择最适宜统计方法的指导原则。为展示不同模型得出的生态学见解差异，我们通过案例研究将各模型应用于单只环斑海豹的运动轨迹。研究表明：不同模型会得出相异的生态学洞见。例如，虽然RSF的选择系数值似乎比SSF显示出与猎物多样性更强的正相关性，但当考虑数据自相关性后，这些与猎物多样性的关系均无统计学显著性。HMM则揭示了不同行为状态下与猎物多样性的可变关联。值得注意的是，三种模型识别出的重要区域各不相同。本案例研究凸显了选择合适模型作为识别物种-栖息地关系和特定重要区域过程中关键步骤的重要意义。本综述为选择最适宜统计方法以解决特定问题（如划定保护区、理解运动模式或研究行为特征）提供了决策所需的基础信息。