Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence
Diving behaviour of narwhals is still largely unknown. We use Hidden Markov models (HMMs) to describe the diving behaviour of a narwhal and fit the models to a three-dimensional response vector of maximum dive depth, duration of dives and post-dive surface time of 8,609 dives measured in East Greenl...
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ftpubmed:oai:pubmedcentral.nih.gov:6417660 2023-05-15T16:03:46+02:00 Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence Ngô, Manh Cuong Heide-Jørgensen, Mads Peter Ditlevsen, Susanne 2019-03-14 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417660/ http://www.ncbi.nlm.nih.gov/pubmed/30870414 https://doi.org/10.1371/journal.pcbi.1006425 en eng Public Library of Science http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417660/ http://www.ncbi.nlm.nih.gov/pubmed/30870414 http://dx.doi.org/10.1371/journal.pcbi.1006425 © 2019 Ngô et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. CC-BY Research Article Text 2019 ftpubmed https://doi.org/10.1371/journal.pcbi.1006425 2019-04-07T00:30:56Z Diving behaviour of narwhals is still largely unknown. We use Hidden Markov models (HMMs) to describe the diving behaviour of a narwhal and fit the models to a three-dimensional response vector of maximum dive depth, duration of dives and post-dive surface time of 8,609 dives measured in East Greenland over 83 days, an extraordinarily long and rich data set. Narwhal diving patterns have not been analysed like this before, but in studies of other whale species, response variables have been assumed independent. We extend the existing models to allow for dependence between state distributions, and show that the dependence has an impact on the conclusions drawn about the diving behaviour. We try several HMMs with 2, 3 or 4 states, and with independent and dependent log-normal and gamma distributions, respectively, and different covariates to characterize dive patterns. In particular, diurnal patterns in diving behaviour is inferred, by using periodic B-splines with boundary knots in 0 and 24 hours. Text East Greenland Greenland narwhal* PubMed Central (PMC) Greenland PLOS Computational Biology 15 3 e1006425 |
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Research Article Ngô, Manh Cuong Heide-Jørgensen, Mads Peter Ditlevsen, Susanne Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence |
topic_facet |
Research Article |
description |
Diving behaviour of narwhals is still largely unknown. We use Hidden Markov models (HMMs) to describe the diving behaviour of a narwhal and fit the models to a three-dimensional response vector of maximum dive depth, duration of dives and post-dive surface time of 8,609 dives measured in East Greenland over 83 days, an extraordinarily long and rich data set. Narwhal diving patterns have not been analysed like this before, but in studies of other whale species, response variables have been assumed independent. We extend the existing models to allow for dependence between state distributions, and show that the dependence has an impact on the conclusions drawn about the diving behaviour. We try several HMMs with 2, 3 or 4 states, and with independent and dependent log-normal and gamma distributions, respectively, and different covariates to characterize dive patterns. In particular, diurnal patterns in diving behaviour is inferred, by using periodic B-splines with boundary knots in 0 and 24 hours. |
format |
Text |
author |
Ngô, Manh Cuong Heide-Jørgensen, Mads Peter Ditlevsen, Susanne |
author_facet |
Ngô, Manh Cuong Heide-Jørgensen, Mads Peter Ditlevsen, Susanne |
author_sort |
Ngô, Manh Cuong |
title |
Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence |
title_short |
Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence |
title_full |
Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence |
title_fullStr |
Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence |
title_full_unstemmed |
Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence |
title_sort |
understanding narwhal diving behaviour using hidden markov models with dependent state distributions and long range dependence |
publisher |
Public Library of Science |
publishDate |
2019 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417660/ http://www.ncbi.nlm.nih.gov/pubmed/30870414 https://doi.org/10.1371/journal.pcbi.1006425 |
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Greenland |
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Greenland |
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East Greenland Greenland narwhal* |
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East Greenland Greenland narwhal* |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417660/ http://www.ncbi.nlm.nih.gov/pubmed/30870414 http://dx.doi.org/10.1371/journal.pcbi.1006425 |
op_rights |
© 2019 Ngô et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
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CC-BY |
op_doi |
https://doi.org/10.1371/journal.pcbi.1006425 |
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PLOS Computational Biology |
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15 |
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e1006425 |
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