Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

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

Clarke, T.M., Whitmarsh, S.K., Hounslow, J.L., Gleiss, A.C., Payne, N.L. and Huveneers, C. (2021) Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish. Movement Ecology, 9 (1). Art. 26.

[img]
Preview
PDF - Published Version
Download (1MB) | Preview
Free to read: https://doi.org/10.1186/s40462-021-00248-8
*No subscription required

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.

Item Type: Journal Article
Murdoch Affiliation(s): College of Science, Health, Engineering and Education
Centre for Sustainable Aquatic Ecosystems
Harry Butler Institute
Publisher: BioMed Central
Copyright: © 2021 The Authors.
United Nations SDGs: Goal 14: Life Below Water
URI: http://researchrepository.murdoch.edu.au/id/eprint/61080
Item Control Page Item Control Page

Downloads

Downloads per month over past year