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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2019
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Online Access: | https://curis.ku.dk/portal/da/publications/understanding-narwhal-diving-behaviour-using-hidden-markov-models-with-dependent-state-distributions-and-long-range-dependence(8efa2bfe-6799-4d8d-b06e-1915cbaec612).html https://doi.org/10.1371/journal.pcbi.1006425 https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006425&type=printable |
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ftcopenhagenunip:oai:pure.atira.dk:publications/8efa2bfe-6799-4d8d-b06e-1915cbaec612 2024-04-28T08:17:24+00:00 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 https://curis.ku.dk/portal/da/publications/understanding-narwhal-diving-behaviour-using-hidden-markov-models-with-dependent-state-distributions-and-long-range-dependence(8efa2bfe-6799-4d8d-b06e-1915cbaec612).html https://doi.org/10.1371/journal.pcbi.1006425 https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006425&type=printable eng eng info:eu-repo/semantics/openAccess Ngô , M C , Heide-jørgensen , M P & Ditlevsen , S 2019 , ' Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence ' , PLOS Computational Biology , vol. 15 , no. 3 , e1006425 . https://doi.org/10.1371/journal.pcbi.1006425 article 2019 ftcopenhagenunip https://doi.org/10.1371/journal.pcbi.1006425 2024-04-04T17:35:13Z 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. Article in Journal/Newspaper East Greenland Greenland narwhal* University of Copenhagen: Research PLOS Computational Biology 15 3 e1006425 |
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Open Polar |
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University of Copenhagen: Research |
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ftcopenhagenunip |
language |
English |
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 |
Article in Journal/Newspaper |
author |
Ngô, Manh Cuong Heide-jørgensen, Mads Peter Ditlevsen, Susanne |
spellingShingle |
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 |
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 |
publishDate |
2019 |
url |
https://curis.ku.dk/portal/da/publications/understanding-narwhal-diving-behaviour-using-hidden-markov-models-with-dependent-state-distributions-and-long-range-dependence(8efa2bfe-6799-4d8d-b06e-1915cbaec612).html https://doi.org/10.1371/journal.pcbi.1006425 https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006425&type=printable |
genre |
East Greenland Greenland narwhal* |
genre_facet |
East Greenland Greenland narwhal* |
op_source |
Ngô , M C , Heide-jørgensen , M P & Ditlevsen , S 2019 , ' Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence ' , PLOS Computational Biology , vol. 15 , no. 3 , e1006425 . https://doi.org/10.1371/journal.pcbi.1006425 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1371/journal.pcbi.1006425 |
container_title |
PLOS Computational Biology |
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15 |
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3 |
container_start_page |
e1006425 |
_version_ |
1797582020177559552 |