Step-selection models are widely used to study animals' fine-scale habitat selection based on movement data. Resource preferences and movement patterns, however, can depend on the animal's unobserved behavioural states, such as resting or foraging. This is ignored in standard (integrated) step-selection analyses (SSA, iSSA). While different approaches have emerged to account for such states in the analysis, the performance of such approaches and the consequences of ignoring the states in the analysis have rarely been quantified. We evaluated the recent idea of combining hidden Markov chains and iSSA in a single modelling framework. The resulting Markov-switching integrated step-selection analysis (MS-iSSA) allows for a joint estimation of both the underlying behavioural states and the associated state-dependent habitat selection. In an extensive simulation study, we compared the MS-iSSA to both the standard iSSA and a classification-based iSSA (i.e., a two-step approach based on a separate prior state classification). We further illustrate the three approaches in a case study on fine-scale interactions of simultaneously tracked bank voles (Myodes glareolus). The results indicate that standard iSSAs can lead to erroneous conclusions due to both biased estimates and unreliable p-values when ignoring underlying behavioural states. We found the same for iSSAs based on prior state-classifications, as they ignore misclassifications and classification uncertainties. The MS-iSSA, on the other hand, performed well in parameter estimation and decoding of behavioural states. To facilitate its use, we implemented the MS-iSSA approach in the R package msissa available on GitHub.

翻译：步骤选择模型被广泛用于基于运动数据研究动物精细尺度的栖息地选择。然而，资源偏好和运动模式可能取决于动物未被观察到的行为状态，例如休息或觅食。这一点在标准的（集成）步骤选择分析（SSA，iSSA）中被忽略。尽管已有多种方法在分析中考虑这些状态，但这些方法的性能以及忽略状态在分析中的后果却很少被量化。我们评估了将隐马尔可夫链与iSSA结合在一个统一建模框架中的最新思路。由此产生的马尔可夫切换集成步骤选择分析（MS-iSSA）允许联合估计潜在的行为状态以及相关的状态依赖型栖息地选择。在一项广泛的模拟研究中，我们将MS-iSSA与标准iSSA以及基于分类的iSSA（即基于单独先验状态分类的两步方法）进行了比较。我们还在一项关于同时追踪的岸田鼠（*Myodes glareolus*）精细尺度互动的案例研究中阐述了这三种方法。结果表明，当忽略潜在的行为状态时，标准iSSA会因有偏估计和不可靠的p值而导致错误结论。我们发现基于先验状态分类的iSSA也存在同样的问题，因为它们忽略了错误分类和分类不确定性。相比之下，MS-iSSA在参数估计和行为状态解码方面表现良好。为便于使用，我们在GitHub上提供的R包msissa中实现了MS-iSSA方法。