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Flexible hidden Markov models for behaviour-dependent habitat selection

BACKGROUND: There is strong incentive to model behaviour-dependent habitat selection, as this can help delineate critical habitats for important life processes and reduce bias in model parameters. For this purpose, a two-stage modelling approach is often taken: (i) classify behaviours with a hidden...

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Autores principales: Klappstein, N. J., Thomas, L., Michelot, T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239607/
https://www.ncbi.nlm.nih.gov/pubmed/37270509
http://dx.doi.org/10.1186/s40462-023-00392-3
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author Klappstein, N. J.
Thomas, L.
Michelot, T.
author_facet Klappstein, N. J.
Thomas, L.
Michelot, T.
author_sort Klappstein, N. J.
collection PubMed
description BACKGROUND: There is strong incentive to model behaviour-dependent habitat selection, as this can help delineate critical habitats for important life processes and reduce bias in model parameters. For this purpose, a two-stage modelling approach is often taken: (i) classify behaviours with a hidden Markov model (HMM), and (ii) fit a step selection function (SSF) to each subset of data. However, this approach does not properly account for the uncertainty in behavioural classification, nor does it allow states to depend on habitat selection. An alternative approach is to estimate both state switching and habitat selection in a single, integrated model called an HMM-SSF. METHODS: We build on this recent methodological work to make the HMM-SSF approach more efficient and general. We focus on writing the model as an HMM where the observation process is defined by an SSF, such that well-known inferential methods for HMMs can be used directly for parameter estimation and state classification. We extend the model to include covariates on the HMM transition probabilities, allowing for inferences into the temporal and individual-specific drivers of state switching. We demonstrate the method through an illustrative example of plains zebra (Equus quagga), including state estimation, and simulations to estimate a utilisation distribution. RESULTS: In the zebra analysis, we identified two behavioural states, with clearly distinct patterns of movement and habitat selection (“encamped” and “exploratory”). In particular, although the zebra tended to prefer areas higher in grassland across both behavioural states, this selection was much stronger in the fast, directed exploratory state. We also found a clear diel cycle in behaviour, which indicated that zebras were more likely to be exploring in the morning and encamped in the evening. CONCLUSIONS: This method can be used to analyse behaviour-specific habitat selection in a wide range of species and systems. A large suite of statistical extensions and tools developed for HMMs and SSFs can be applied directly to this integrated model, making it a very versatile framework to jointly learn about animal behaviour, habitat selection, and space use. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40462-023-00392-3.
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spelling pubmed-102396072023-06-05 Flexible hidden Markov models for behaviour-dependent habitat selection Klappstein, N. J. Thomas, L. Michelot, T. Mov Ecol Methodology BACKGROUND: There is strong incentive to model behaviour-dependent habitat selection, as this can help delineate critical habitats for important life processes and reduce bias in model parameters. For this purpose, a two-stage modelling approach is often taken: (i) classify behaviours with a hidden Markov model (HMM), and (ii) fit a step selection function (SSF) to each subset of data. However, this approach does not properly account for the uncertainty in behavioural classification, nor does it allow states to depend on habitat selection. An alternative approach is to estimate both state switching and habitat selection in a single, integrated model called an HMM-SSF. METHODS: We build on this recent methodological work to make the HMM-SSF approach more efficient and general. We focus on writing the model as an HMM where the observation process is defined by an SSF, such that well-known inferential methods for HMMs can be used directly for parameter estimation and state classification. We extend the model to include covariates on the HMM transition probabilities, allowing for inferences into the temporal and individual-specific drivers of state switching. We demonstrate the method through an illustrative example of plains zebra (Equus quagga), including state estimation, and simulations to estimate a utilisation distribution. RESULTS: In the zebra analysis, we identified two behavioural states, with clearly distinct patterns of movement and habitat selection (“encamped” and “exploratory”). In particular, although the zebra tended to prefer areas higher in grassland across both behavioural states, this selection was much stronger in the fast, directed exploratory state. We also found a clear diel cycle in behaviour, which indicated that zebras were more likely to be exploring in the morning and encamped in the evening. CONCLUSIONS: This method can be used to analyse behaviour-specific habitat selection in a wide range of species and systems. A large suite of statistical extensions and tools developed for HMMs and SSFs can be applied directly to this integrated model, making it a very versatile framework to jointly learn about animal behaviour, habitat selection, and space use. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40462-023-00392-3. BioMed Central 2023-06-03 /pmc/articles/PMC10239607/ /pubmed/37270509 http://dx.doi.org/10.1186/s40462-023-00392-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology
Klappstein, N. J.
Thomas, L.
Michelot, T.
Flexible hidden Markov models for behaviour-dependent habitat selection
title Flexible hidden Markov models for behaviour-dependent habitat selection
title_full Flexible hidden Markov models for behaviour-dependent habitat selection
title_fullStr Flexible hidden Markov models for behaviour-dependent habitat selection
title_full_unstemmed Flexible hidden Markov models for behaviour-dependent habitat selection
title_short Flexible hidden Markov models for behaviour-dependent habitat selection
title_sort flexible hidden markov models for behaviour-dependent habitat selection
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239607/
https://www.ncbi.nlm.nih.gov/pubmed/37270509
http://dx.doi.org/10.1186/s40462-023-00392-3
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