Behavioural state inference from movement and environmental data using Markovian step selection functions
- 1Mathematics Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
- 2Département de biologie, Université Laval, Québec (Qc), Canada
- 3Interdisciplinary Research Center for Biosystems and Machines, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Abstract
Animal movement data often contain multiple behavioural states that vary across space and time, but identifying these states in a biologically interpretable way remains challenging. We propose an ecologically guided framework for inferring individual behavioural states from movement trajectories using hidden Markov models with step selection functions (HMM-SSFs). The method combines a small set of a priori, biologically motivated state hypotheses with explicit ordering and distinctness criteria on movement signatures, together with model-adequacy diagnostics and a stable fitting strategy. Unlike fully unsupervised approaches, our framework is designed to select among a limited number of ecologically meaningful behavioural states rather than exhaustively search a large latent state space. Simulations show that the method can recover the intended state structure under different behavioural scenarios, and that inference is sensitive to the specification of control locations in the SSF component. An illustrative application to a plains zebra trajectory demonstrates how the framework can be used to infer and label behavioural states from movement data. Overall, the proposed workflow provides a practical and reproducible way to infer individual-level behavioural states while maintaining ecological interpretability.
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BibTeX
@article{bouderbala2026behavioural,
title = {{Behavioural state inference from movement and environmental data using Markovian step selection functions}},
author = {Ilhem Bouderbala and Aurélien Nicosia and Daniel Fortin},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0360547},
url = {https://doi.org/10.1371/journal.pone.0360547},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Behavioural state inference from movement and environmental data using Markovian step selection functions
AU - Ilhem Bouderbala
AU - Aurélien Nicosia
AU - Daniel Fortin
PY - 2026
DA - 2026/10/26
JO - PLOS One
DO - 10.1371/journal.pone.0360547
UR - https://doi.org/10.1371/journal.pone.0360547
AB - Animal movement data often contain multiple behavioural states that vary across space and time, but identifying these states in a biologically interpretable way remains challenging. We propose an ecologically guided framework for inferring individual behavioural states from movement trajectories using hidden Markov models with step selection functions (HMM-SSFs). The method combines a small set of a priori, biologically motivated state hypotheses with explicit ordering and distinctness criteria on movement signatures, together with model-adequacy diagnostics and a stable fitting strategy. Unlike fully unsupervised approaches, our framework is designed to select among a limited number of ecologically meaningful behavioural states rather than exhaustively search a large latent state space. Simulations show that the method can recover the intended state structure under different behavioural scenarios, and that inference is sensitive to the specification of control locations in the SSF component. An illustrative application to a plains zebra trajectory demonstrates how the framework can be used to infer and label behavioural states from movement data. Overall, the proposed workflow provides a practical and reproducible way to infer individual-level behavioural states while maintaining ecological interpretability.
SN - 1932-6203
ER -