Validating hidden Markov models for seabird behavioural inference.
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| Title: | Validating hidden Markov models for seabird behavioural inference. |
|---|---|
| Authors: | Akeresola, Rebecca A.1,2 (AUTHOR) r.a.akeresola@sms.ed.ac.uk, Butler, Adam2 (AUTHOR), Jones, Esther L.2 (AUTHOR), King, Ruth1 (AUTHOR), Elvira, Víctor1 (AUTHOR), Black, Julie3 (AUTHOR), Robertson, Gail2 (AUTHOR) |
| Source: | Ecology & Evolution (20457758). Mar2024, Vol. 14 Issue 3, p1-14. 14p. |
| Subject Terms: | *Wildlife conservation, *Nature conservation, *Animal behavior, *Protected areas, Hidden Markov models, Animal mechanics, Chicks |
| Abstract: | Understanding animal movement and behaviour can aid spatial planning and inform conservation management. However, it is difficult to directly observe behaviours in remote and hostile terrain such as the marine environment. Different underlying states can be identified from telemetry data using hidden Markov models (HMMs). The inferred states are subsequently associated with different behaviours, using ecological knowledge of the species. However, the inferred behaviours are not typically validated due to difficulty obtaining 'ground truth' behavioural information. We investigate the accuracy of inferred behaviours by considering a unique data set provided by Joint Nature Conservation Committee. The data consist of simultaneous proxy movement tracks of the boat (defined as visual tracks as birds are followed by eye) and seabird behaviour obtained by observers on the boat. We demonstrate that visual tracking data is suitable for our study. Accuracy of HMMs ranging from 71% to 87% during chick‐rearing and 54% to 70% during incubation was generally insensitive to model choice, even when AIC values varied substantially across different models. Finally, we show that for foraging, a state of primary interest for conservation purposes, identified missed foraging bouts lasted for only a few seconds. We conclude that HMMs fitted to tracking data have the potential to accurately identify important conservation‐relevant behaviours, demonstrated by a comparison in which visual tracking data provide a 'gold standard' of manually classified behaviours to validate against. Confidence in using HMMs for behavioural inference should increase as a result of these findings, but future work is needed to assess the generalisability of the results, and we recommend that, wherever feasible, validation data be collected alongside GPS tracking data to validate model performance. This work has important implications for animal conservation, where the size and location of protected areas are often informed by behaviours identified using HMMs fitted to movement data. [ABSTRACT FROM AUTHOR] |
| Copyright of Ecology & Evolution (20457758) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 176245746 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Validating hidden Markov models for seabird behavioural inference. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Akeresola%2C+Rebecca+A%2E%22">Akeresola, Rebecca A.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> r.a.akeresola@sms.ed.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Butler%2C+Adam%22">Butler, Adam</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jones%2C+Esther+L%2E%22">Jones, Esther L.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22King%2C+Ruth%22">King, Ruth</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Elvira%2C+Víctor%22">Elvira, Víctor</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Black%2C+Julie%22">Black, Julie</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Robertson%2C+Gail%22">Robertson, Gail</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Ecology+%26+Evolution+%2820457758%29%22">Ecology & Evolution (20457758)</searchLink>. Mar2024, Vol. 14 Issue 3, p1-14. 14p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Wildlife+conservation%22">Wildlife conservation</searchLink><br />*<searchLink fieldCode="DE" term="%22Nature+conservation%22">Nature conservation</searchLink><br />*<searchLink fieldCode="DE" term="%22Animal+behavior%22">Animal behavior</searchLink><br />*<searchLink fieldCode="DE" term="%22Protected+areas%22">Protected areas</searchLink><br /><searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink><br /><searchLink fieldCode="DE" term="%22Animal+mechanics%22">Animal mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Chicks%22">Chicks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Understanding animal movement and behaviour can aid spatial planning and inform conservation management. However, it is difficult to directly observe behaviours in remote and hostile terrain such as the marine environment. Different underlying states can be identified from telemetry data using hidden Markov models (HMMs). The inferred states are subsequently associated with different behaviours, using ecological knowledge of the species. However, the inferred behaviours are not typically validated due to difficulty obtaining 'ground truth' behavioural information. We investigate the accuracy of inferred behaviours by considering a unique data set provided by Joint Nature Conservation Committee. The data consist of simultaneous proxy movement tracks of the boat (defined as visual tracks as birds are followed by eye) and seabird behaviour obtained by observers on the boat. We demonstrate that visual tracking data is suitable for our study. Accuracy of HMMs ranging from 71% to 87% during chick‐rearing and 54% to 70% during incubation was generally insensitive to model choice, even when AIC values varied substantially across different models. Finally, we show that for foraging, a state of primary interest for conservation purposes, identified missed foraging bouts lasted for only a few seconds. We conclude that HMMs fitted to tracking data have the potential to accurately identify important conservation‐relevant behaviours, demonstrated by a comparison in which visual tracking data provide a 'gold standard' of manually classified behaviours to validate against. Confidence in using HMMs for behavioural inference should increase as a result of these findings, but future work is needed to assess the generalisability of the results, and we recommend that, wherever feasible, validation data be collected alongside GPS tracking data to validate model performance. This work has important implications for animal conservation, where the size and location of protected areas are often informed by behaviours identified using HMMs fitted to movement data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Ecology & Evolution (20457758) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/ece3.11116 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Wildlife conservation Type: general – SubjectFull: Nature conservation Type: general – SubjectFull: Animal behavior Type: general – SubjectFull: Protected areas Type: general – SubjectFull: Hidden Markov models Type: general – SubjectFull: Animal mechanics Type: general – SubjectFull: Chicks Type: general Titles: – TitleFull: Validating hidden Markov models for seabird behavioural inference. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Akeresola, Rebecca A. – PersonEntity: Name: NameFull: Butler, Adam – PersonEntity: Name: NameFull: Jones, Esther L. – PersonEntity: Name: NameFull: King, Ruth – PersonEntity: Name: NameFull: Elvira, Víctor – PersonEntity: Name: NameFull: Black, Julie – PersonEntity: Name: NameFull: Robertson, Gail IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 20457758 Numbering: – Type: volume Value: 14 – Type: issue Value: 3 Titles: – TitleFull: Ecology & Evolution (20457758) Type: main |
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