Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence

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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.

In: PLOS Computational Biology, Vol. 15, No. 3, e1006425, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Ngô, MC, Heide-jørgensen, MP & 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

APA

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, 15(3), [e1006425]. https://doi.org/10.1371/journal.pcbi.1006425

Vancouver

Ngô MC, Heide-jørgensen MP, Ditlevsen S. Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence. PLOS Computational Biology. 2019;15(3). e1006425. https://doi.org/10.1371/journal.pcbi.1006425

Author

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. In: PLOS Computational Biology. 2019 ; Vol. 15, No. 3.

Bibtex

@article{8efa2bfe67994d8db06e1915cbaec612,
title = "Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence",
abstract = "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.",
author = "Ng{\^o}, {Manh Cuong} and Heide-j{\o}rgensen, {Mads Peter} and Susanne Ditlevsen",
year = "2019",
doi = "10.1371/journal.pcbi.1006425",
language = "English",
volume = "15",
journal = "P L o S Computational Biology (Online)",
issn = "1553-7358",
publisher = "Public Library of Science",
number = "3",

}

RIS

TY - JOUR

T1 - Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence

AU - Ngô, Manh Cuong

AU - Heide-jørgensen, Mads Peter

AU - Ditlevsen, Susanne

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pcbi.1006425

DO - 10.1371/journal.pcbi.1006425

M3 - Journal article

C2 - 30870414

VL - 15

JO - P L o S Computational Biology (Online)

JF - P L o S Computational Biology (Online)

SN - 1553-7358

IS - 3

M1 - e1006425

ER -

ID: 215090648