Courtney R. Shuert
Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal
Shuert, Courtney R.; Pomeroy, Patrick P.; Twiss, Sean D.
Abstract
Background Classifying behaviour with animal-borne accelerometers is quickly becoming a popular tool for remotely observing behavioural states in a variety of species. Most accelerometry work in pinnipeds has focused on classifying behaviour at sea often quantifying behavioural trade-offs associated with foraging and diving in income breeders. Very little work to date has been done to resolve behaviour during the critical period of lactation in a capital breeder. Capital breeding phocids possess finite reserves that they must allocate appropriately to maintain themselves and their new offspring during their brief nursing period. Within this short time, fine-scale behavioural trade-offs can have significant fitness consequences for mother and offspring and must be carefully managed. Here, we present a case study in extracting and classifying lactation behaviours in a wild, breeding pinniped, the grey seal (Halichoerus grypus). Results Using random forest models, we were able to resolve 4 behavioural states that constitute the majority of a female grey seals’ activity budget during lactation. Resting, alert, nursing, and a form of pup interaction were extracted and classified reliably. For the first time, we quantified the potential confounding variance associated with individual differences in a wild context as well as differences due to sampling location in a largely inactive model species. Conclusions At this stage, the majority of a female grey seal’s activity budget was classified well using accelerometers, but some rare and context-dependent behaviours were not well captured. While we did find significant variation between individuals in behavioural mechanics, individuals did not differ significantly within themselves; inter-individual variability should be an important consideration in future efforts. These methods can be extended to other efforts to study grey seals and other pinnipeds who exhibit a capital breeding system. Using accelerometers to classify behaviour during lactation allows for fine-scale assessments of time and energy trade-offs for species with fixed stores.
Citation
Shuert, C. R., Pomeroy, P. P., & Twiss, S. D. (2018). Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal. Animal Biotelemetry, 6(1), Article 14. https://doi.org/10.1186/s40317-018-0158-y
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 8, 2018 |
Online Publication Date | Oct 16, 2018 |
Publication Date | Oct 16, 2018 |
Deposit Date | Oct 17, 2018 |
Publicly Available Date | Oct 18, 2018 |
Journal | Animal Biotelemetry |
Electronic ISSN | 2050-3385 |
Publisher | BioMed Central |
Peer Reviewed | Peer Reviewed |
Volume | 6 |
Issue | 1 |
Article Number | 14 |
DOI | https://doi.org/10.1186/s40317-018-0158-y |
Public URL | https://durham-repository.worktribe.com/output/1345456 |
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© The Author(s) 2018 This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/
publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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