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Published (Version of Record)CC BY V4.0, Open Access
Journal article
Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish
Movement Ecology, Vol.9(1), 26
2021
Abstract
Background
Tri-axial accelerometers have been used to remotely describe and identify in situ behaviours of a range of animals without requiring direct observations. Datasets collected from these accelerometers (i.e. acceleration, body position) are often large, requiring development of semi-automated analyses to classify behaviours. Marine fishes exhibit many “burst” behaviours with high amplitude accelerations that are difficult to interpret and differentiate. This has constrained the development of accurate automated techniques to identify different “burst” behaviours occurring naturally, where direct observations are not possible.
Methods
We trained a random forest machine learning algorithm based on 624 h of accelerometer data from six captive yellowtail kingfish during spawning periods. We identified five distinct behaviours (swim, feed, chafe, escape, and courtship), which were used to train the model based on 58 predictive variables.
Results
Overall accuracy of the model was 94%. Classification of each behavioural class was variable; F1 scores ranged from 0.48 (chafe) – 0.99 (swim). The model was subsequently applied to accelerometer data from eight free-ranging kingfish, and all behaviour classes described from captive fish were predicted by the model to occur, including 19 events of courtship behaviours ranging from 3 s to 108 min in duration.
Conclusion
Our findings provide a novel approach of applying a supervised machine learning model on free-ranging animals, which has previously been predominantly constrained to direct observations of behaviours and not predicted from an unseen dataset. Additionally, our findings identify typically ambiguous spawning and courtship behaviours of a large pelagic fish as they naturally occur.
Details
- Title
- Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish
- Authors/Creators
- T.M. Clarke (Author/Creator) - Flinders UniversityS.K. Whitmarsh (Author/Creator) - Flinders UniversityJ.L. Hounslow (Author/Creator) - Murdoch UniversityA.C. Gleiss (Author/Creator) - Murdoch UniversityN.L. Payne (Author/Creator) - Trinity College DublinC. Huveneers (Author/Creator)
- Publication Details
- Movement Ecology, Vol.9(1), 26
- Publisher
- BioMed Central
- Identifiers
- 991005540794507891
- Copyright
- © 2021 The Authors.
- Murdoch Affiliation
- Centre for Sustainable Aquatic Ecosystems; College of Science, Health, Engineering and Education; Harry Butler Institute
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.2 Marine Biology
- 3.2.92 Fisheries Ecology
- Web Of Science research areas
- Ecology
- ESI research areas
- Environment/Ecology