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