ArticlePeerJ2024
How to account for behavioral states in step-selection analysis: a model comparison.
Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Mechanisms of animal distribution: bridging advances in movement analysis with hierarchical and optimal foraging theories.Landscape ecology · 2026Article
- Considering landscape heterogeneity improves the inference of inter-individual interactions from movement data.Movement ecology · 2025Article
- The 4th Dimension in Animal Movement: The Effect of Temporal Resolution and Landscape Configuration in Habitat-Selection Analyses.Ecology and evolution · 2025Article
- Methods for implementing integrated step-selection functions with incomplete data.Movement ecology · 2024Article
- Flexible hidden Markov models for behaviour-dependent habitat selection.Movement ecology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Step-selection models are widely used to study animals' fine-scale habitat selection based on movement data. Resource preferences and movement patterns, however, often depend on the animal's unobserved behavioral states, such as resting or foraging. As this is ignored in standard (integrated) step-selection analyses (SSA, iSSA), different approaches have emerged to account for such states in the analysis. The performance of these approaches and the consequences of ignoring the states in step-selection analysis, however, have rarely been quantified. We evaluate the recent idea of combining iSSAs with hidden Markov models (HMMs), which allows for a joint estimation of the unobserved behavioral states and the associated state-dependent habitat selection. Besides theoretical considerations, we use an extensive simulation study and a case study on fine-scale interactions of simultaneously tracked bank voles (
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