Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish

• Published in: Movement ecology 2021-05, Vol.9 (1), p.1-26, Article 26
• Main Authors: Clarke, Thomas M, Whitmarsh, Sasha K, Hounslow, Jenna L, Gleiss, Adrian C, Payne, Nicholas L, Huveneers, Charlie
• Format: Article
• Language: English
• Subjects: Accelerometers; Algorithms; Animals; Aquaculture; Behavior; Biologging; Cameras; Captive; Classification; Courtship; Data mining; Datasets; Fish; Fishes; Kingfish; Learning algorithms; Machine learning; Marine fish; Model accuracy; Spawning; Spawning behavior
• ISSN: 2051-3933; 2051-3933
• DOI: 10.1186/s40462-021-00248-8

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; F.sub.1 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. Keywords: Biologging, Courtship, Kingfish, Captive, Machine learning