A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure
Characterization of multivariate time series of behaviour data from animal-borne sensors is challenging. Biologists require methods to objectively quantify baseline behaviour, and then assess behaviour changes in response to environmental stimuli. Here, we apply hidden Markov models (HMMs) to charac...
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ftculeuclid:oai:CULeuclid:euclid.aoas/1491616885 2023-05-15T15:45:07+02:00 A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure DeRuiter, Stacy L. Langrock, Roland Skirbutas, Tomas Goldbogen, Jeremy A. Calambokidis, John Friedlaender, Ari S. Southall, Brandon L. 2017-03 application/pdf http://projecteuclid.org/euclid.aoas/1491616885 https://doi.org/10.1214/16-AOAS1008 en eng The Institute of Mathematical Statistics 1932-6157 1941-7330 Copyright 2017 Institute of Mathematical Statistics Forward algorithm hidden Markov model multivariate time series numerical maximum likelihood random effects blue whales Text 2017 ftculeuclid https://doi.org/10.1214/16-AOAS1008 2018-10-06T13:03:04Z Characterization of multivariate time series of behaviour data from animal-borne sensors is challenging. Biologists require methods to objectively quantify baseline behaviour, and 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 a more nuanced characterization of behaviour changes. Text Blue whale Project Euclid (Cornell University Library) The Annals of Applied Statistics 11 1 |
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Open Polar |
collection |
Project Euclid (Cornell University Library) |
op_collection_id |
ftculeuclid |
language |
English |
topic |
Forward algorithm hidden Markov model multivariate time series numerical maximum likelihood random effects blue whales |
spellingShingle |
Forward algorithm hidden Markov model multivariate time series numerical maximum likelihood random effects blue whales DeRuiter, Stacy L. Langrock, Roland Skirbutas, Tomas Goldbogen, Jeremy A. Calambokidis, John Friedlaender, Ari S. Southall, Brandon L. A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure |
topic_facet |
Forward algorithm hidden Markov model multivariate time series numerical maximum likelihood random effects blue whales |
description |
Characterization of multivariate time series of behaviour data from animal-borne sensors is challenging. Biologists require methods to objectively quantify baseline behaviour, and 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 a more nuanced characterization of behaviour changes. |
format |
Text |
author |
DeRuiter, Stacy L. Langrock, Roland Skirbutas, Tomas Goldbogen, Jeremy A. Calambokidis, John Friedlaender, Ari S. Southall, Brandon L. |
author_facet |
DeRuiter, Stacy L. Langrock, Roland Skirbutas, Tomas Goldbogen, Jeremy A. Calambokidis, John Friedlaender, Ari S. Southall, Brandon L. |
author_sort |
DeRuiter, Stacy L. |
title |
A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure |
title_short |
A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure |
title_full |
A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure |
title_fullStr |
A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure |
title_full_unstemmed |
A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure |
title_sort |
multivariate mixed hidden markov model for blue whale behaviour and responses to sound exposure |
publisher |
The Institute of Mathematical Statistics |
publishDate |
2017 |
url |
http://projecteuclid.org/euclid.aoas/1491616885 https://doi.org/10.1214/16-AOAS1008 |
genre |
Blue whale |
genre_facet |
Blue whale |
op_relation |
1932-6157 1941-7330 |
op_rights |
Copyright 2017 Institute of Mathematical Statistics |
op_doi |
https://doi.org/10.1214/16-AOAS1008 |
container_title |
The Annals of Applied Statistics |
container_volume |
11 |
container_issue |
1 |
_version_ |
1766379476709539840 |