A multivariate mixed hidden Markov model to analyze blue whale diving behaviour during controlled sound exposures
Characterization of multivariate time series of behaviour data from animal-borne sensors is challenging. Biologists require methods to objectively quantify baseline behaviour, then assess behaviour changes in response to environmental stimuli. Here, we apply hidden Markov models (HMMs) to characteri...
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Online Access: | https://dx.doi.org/10.48550/arxiv.1602.06570 https://arxiv.org/abs/1602.06570 |
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ftdatacite:10.48550/arxiv.1602.06570 2023-05-15T15:45:06+02:00 A multivariate mixed hidden Markov model to analyze blue whale diving behaviour during controlled sound exposures DeRuiter, Stacy L Langrock, Roland Skirbutas, Tomas Goldbogen, Jeremy A Chalambokidis, John Friedlaender, Ari S Southall, Brandon L 2016 https://dx.doi.org/10.48550/arxiv.1602.06570 https://arxiv.org/abs/1602.06570 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Applications stat.AP Quantitative Methods q-bio.QM FOS Computer and information sciences FOS Biological sciences Preprint Article article CreativeWork 2016 ftdatacite https://doi.org/10.48550/arxiv.1602.06570 2022-04-01T11:42:30Z Characterization of multivariate time series of behaviour data from animal-borne sensors is challenging. Biologists require methods to objectively quantify baseline behaviour, then assess behaviour changes in response to environmental stimuli. Here, we apply hidden Markov models (HMMs) to characterize blue whale movement and diving behaviour, identifying latent states corresponding to three main underlying behaviour states: shallow feeding, travelling, and deep feeding. The model formulation accounts for inter-whale differences via a computationally efficient discrete random effect, and measures potential effects of experimental acoustic disturbance on between-state transition probabilities. We identify clear differences in blue whale disturbance response depending on the behavioural context during exposure, with whales less likely to initiate deep foraging behaviour during exposure. Findings are consistent with earlier studies using smaller samples, but the HMM approach provides more nuanced characterization of behaviour changes. Report Blue whale DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
Applications stat.AP Quantitative Methods q-bio.QM FOS Computer and information sciences FOS Biological sciences |
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Applications stat.AP Quantitative Methods q-bio.QM FOS Computer and information sciences FOS Biological sciences DeRuiter, Stacy L Langrock, Roland Skirbutas, Tomas Goldbogen, Jeremy A Chalambokidis, John Friedlaender, Ari S Southall, Brandon L A multivariate mixed hidden Markov model to analyze blue whale diving behaviour during controlled sound exposures |
topic_facet |
Applications stat.AP Quantitative Methods q-bio.QM FOS Computer and information sciences FOS Biological sciences |
description |
Characterization of multivariate time series of behaviour data from animal-borne sensors is challenging. Biologists require methods to objectively quantify baseline behaviour, then assess behaviour changes in response to environmental stimuli. Here, we apply hidden Markov models (HMMs) to characterize blue whale movement and diving behaviour, identifying latent states corresponding to three main underlying behaviour states: shallow feeding, travelling, and deep feeding. The model formulation accounts for inter-whale differences via a computationally efficient discrete random effect, and measures potential effects of experimental acoustic disturbance on between-state transition probabilities. We identify clear differences in blue whale disturbance response depending on the behavioural context during exposure, with whales less likely to initiate deep foraging behaviour during exposure. Findings are consistent with earlier studies using smaller samples, but the HMM approach provides more nuanced characterization of behaviour changes. |
format |
Report |
author |
DeRuiter, Stacy L Langrock, Roland Skirbutas, Tomas Goldbogen, Jeremy A Chalambokidis, John Friedlaender, Ari S Southall, Brandon L |
author_facet |
DeRuiter, Stacy L Langrock, Roland Skirbutas, Tomas Goldbogen, Jeremy A Chalambokidis, John Friedlaender, Ari S Southall, Brandon L |
author_sort |
DeRuiter, Stacy L |
title |
A multivariate mixed hidden Markov model to analyze blue whale diving behaviour during controlled sound exposures |
title_short |
A multivariate mixed hidden Markov model to analyze blue whale diving behaviour during controlled sound exposures |
title_full |
A multivariate mixed hidden Markov model to analyze blue whale diving behaviour during controlled sound exposures |
title_fullStr |
A multivariate mixed hidden Markov model to analyze blue whale diving behaviour during controlled sound exposures |
title_full_unstemmed |
A multivariate mixed hidden Markov model to analyze blue whale diving behaviour during controlled sound exposures |
title_sort |
multivariate mixed hidden markov model to analyze blue whale diving behaviour during controlled sound exposures |
publisher |
arXiv |
publishDate |
2016 |
url |
https://dx.doi.org/10.48550/arxiv.1602.06570 https://arxiv.org/abs/1602.06570 |
genre |
Blue whale |
genre_facet |
Blue whale |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1602.06570 |
_version_ |
1766379469952516096 |