{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Mathematics Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia",
    "Département de biologie, Université Laval, Québec (Qc), Canada",
    "Interdisciplinary Research Center for Biosystems and Machines, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ilhem Bouderbala"
    },
    {
      "affs": [
        1
      ],
      "name": "Aurélien Nicosia"
    },
    {
      "affs": [
        2
      ],
      "name": "Daniel Fortin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7d86v0gwvwscm.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0360547",
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  "id": "7d86v0gwvwscm",
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    "1932-6203"
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  "landing_url": "https://doi.org/10.1371/journal.pone.0360547",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "2303|Wildlife Ecology and Conservation"
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      "/Biology and life sciences/Ecology/Habitats"
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      "/Biology and life sciences/Psychology/Behavior/Animal behavior/Foraging"
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    [
      "plos",
      "/Biology and life sciences/Zoology/Animal behavior"
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    [
      "plos",
      "/Biology and life sciences/Zoology/Animal behavior/Foraging"
    ],
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      "plos",
      "/Biology and life sciences/Zoology/Animals/Vertebrates/Amniotes/Mammals/Equines/Zebras"
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      "plos",
      "/Computer and information sciences/Data management/Taxonomy"
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      "/Ecology and environmental sciences/Ecology/Habitats"
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      "/Ecology and environmental sciences/Ecology/Plant ecology/Plant communities/Grasslands"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Terrestrial environments/Grasslands"
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      "plos",
      "/Physical sciences/Mathematics/Probability theory/Markov models/Hidden Markov models"
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    [
      "plos",
      "/Physical sciences/Mathematics/Probability theory/Probability distribution"
    ],
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      "/Social sciences/Psychology/Behavior/Animal behavior"
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      "plos",
      "/Social sciences/Psychology/Behavior/Animal behavior/Foraging"
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  "openalex": "W7223670012",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0360547&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/7d86v0gwvwscm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.1371/journal.pone.0360547",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0360547"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:ecology",
      "subfield": "ecology"
    },
    {
      "field": "biology",
      "origin": "plos:animal behavior",
      "subfield": "zoology-behaviour"
    }
  ],
  "title": "Behavioural state inference from movement and environmental data using Markovian step selection functions",
  "title_text": "Behavioural state inference from movement and environmental data using Markovian step selection functions",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/7d86v0gwvwscm",
  "venue": "PLOS One",
  "versions": []
}