A Hidden Markov Movement Model for rapidly identifying behavioral states from animal tracks

Kim Whoriskey, Marie Auger-Méthé, Christoffer Moesgaard Albertsen, Frederick G. Whoriskey, Thomas R. Binder, Charles C. Krueger, Joanna Mills Flemming

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Abstract

1. Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic animal tracking data with significant measurement error, a Bayesian state-space model called the first-Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data of animal movement are now becoming more common. 2. We developed a new Hidden Markov Model (HMM) for identifying behavioral states from animal tracks with negligible error, which we called the Hidden Markov Movement Model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum likelihood and the R package TMB for rapid model fitting. 3. We compared the HMMM to a modified version of the DCRWS for highly accurate tracks, the DCRWSnome, and to a common HMM for animal tracks fitted with the R package moveHMM. We show that the HMMM is both accurate and suitable for multiple species by fitting it to real tracks from a grey seal, lake trout, and blue shark, as well as to simulated data. 4. The HMMM is a fast and reliable tool for making meaningful inference from animal movement data that is ideally suited for ecologists who want to use the popular DCRWS implementation for highly accurate tracking data. It additionally provides a groundwork for development of more complex modelling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim.
Original languageEnglish
JournalEcology and Evolution
Volume7
Issue number7
Pages (from-to)2112-2121
ISSN2045-7758
DOIs
Publication statusPublished - 2017

Keywords

  • q-bio.QM

Cite this

Whoriskey, K., Auger-Méthé, M., Albertsen, C. M., Whoriskey, F. G., Binder, T. R., Krueger, C. C., & Flemming, J. M. (2017). A Hidden Markov Movement Model for rapidly identifying behavioral states from animal tracks. Ecology and Evolution, 7(7), 2112-2121. https://doi.org/10.1002/ece3.2795
Whoriskey, Kim ; Auger-Méthé, Marie ; Albertsen, Christoffer Moesgaard ; Whoriskey, Frederick G. ; Binder, Thomas R. ; Krueger, Charles C. ; Flemming, Joanna Mills. / A Hidden Markov Movement Model for rapidly identifying behavioral states from animal tracks. In: Ecology and Evolution. 2017 ; Vol. 7, No. 7. pp. 2112-2121.
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abstract = "1. Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic animal tracking data with significant measurement error, a Bayesian state-space model called the first-Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data of animal movement are now becoming more common. 2. We developed a new Hidden Markov Model (HMM) for identifying behavioral states from animal tracks with negligible error, which we called the Hidden Markov Movement Model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum likelihood and the R package TMB for rapid model fitting. 3. We compared the HMMM to a modified version of the DCRWS for highly accurate tracks, the DCRWSnome, and to a common HMM for animal tracks fitted with the R package moveHMM. We show that the HMMM is both accurate and suitable for multiple species by fitting it to real tracks from a grey seal, lake trout, and blue shark, as well as to simulated data. 4. The HMMM is a fast and reliable tool for making meaningful inference from animal movement data that is ideally suited for ecologists who want to use the popular DCRWS implementation for highly accurate tracking data. It additionally provides a groundwork for development of more complex modelling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim.",
keywords = "q-bio.QM",
author = "Kim Whoriskey and Marie Auger-M{\'e}th{\'e} and Albertsen, {Christoffer Moesgaard} and Whoriskey, {Frederick G.} and Binder, {Thomas R.} and Krueger, {Charles C.} and Flemming, {Joanna Mills}",
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Whoriskey, K, Auger-Méthé, M, Albertsen, CM, Whoriskey, FG, Binder, TR, Krueger, CC & Flemming, JM 2017, 'A Hidden Markov Movement Model for rapidly identifying behavioral states from animal tracks', Ecology and Evolution, vol. 7, no. 7, pp. 2112-2121. https://doi.org/10.1002/ece3.2795

A Hidden Markov Movement Model for rapidly identifying behavioral states from animal tracks. / Whoriskey, Kim; Auger-Méthé, Marie; Albertsen, Christoffer Moesgaard; Whoriskey, Frederick G.; Binder, Thomas R.; Krueger, Charles C.; Flemming, Joanna Mills.

In: Ecology and Evolution, Vol. 7, No. 7, 2017, p. 2112-2121.

Research output: Contribution to journalJournal articleResearchpeer-review

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T1 - A Hidden Markov Movement Model for rapidly identifying behavioral states from animal tracks

AU - Whoriskey, Kim

AU - Auger-Méthé, Marie

AU - Albertsen, Christoffer Moesgaard

AU - Whoriskey, Frederick G.

AU - Binder, Thomas R.

AU - Krueger, Charles C.

AU - Flemming, Joanna Mills

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AB - 1. Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic animal tracking data with significant measurement error, a Bayesian state-space model called the first-Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data of animal movement are now becoming more common. 2. We developed a new Hidden Markov Model (HMM) for identifying behavioral states from animal tracks with negligible error, which we called the Hidden Markov Movement Model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum likelihood and the R package TMB for rapid model fitting. 3. We compared the HMMM to a modified version of the DCRWS for highly accurate tracks, the DCRWSnome, and to a common HMM for animal tracks fitted with the R package moveHMM. We show that the HMMM is both accurate and suitable for multiple species by fitting it to real tracks from a grey seal, lake trout, and blue shark, as well as to simulated data. 4. The HMMM is a fast and reliable tool for making meaningful inference from animal movement data that is ideally suited for ecologists who want to use the popular DCRWS implementation for highly accurate tracking data. It additionally provides a groundwork for development of more complex modelling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim.

KW - q-bio.QM

U2 - 10.1002/ece3.2795

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EP - 2121

JO - Ecology and Evolution

JF - Ecology and Evolution

SN - 2045-7758

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