Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal

Courtney Schuert, Patrick Pomeroy, Sean Twiss

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
3 Downloads (Pure)

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.
Original languageEnglish
Article number14
Number of pages17
JournalAnimal Biotelemetry
Volume6
DOIs
Publication statusPublished - 16 Oct 2018

Keywords

  • Accelerometer
  • Grey seal
  • Maternal behaviour
  • Breeding behaviour
  • Machine learning
  • Classification

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