L. R. Brewster
Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data
Brewster, L. R.; Dale, J. J.; Guttridge, T. L.; Gruber, S. H.; Hansell, A. C.; Elliott, M.; Cowx, I. G.; Whitney, N. M.; Gleiss, A. C.
J. J. Dale
T. L. Guttridge
S. H. Gruber
A. C. Hansell
I. G. Cowx
N. M. Whitney
A. C. Gleiss
© 2018, The Author(s). Discerning behaviours of free-ranging animals allows for quantification of their activity budget, providing important insight into ecology. Over recent years, accelerometers have been used to unveil the cryptic lives of animals. The increased ability of accelerometers to store large quantities of high resolution data has prompted a need for automated behavioural classification. We assessed the performance of several machine learning (ML) classifiers to discern five behaviours performed by accelerometer-equipped juvenile lemon sharks (Negaprion brevirostris) at Bimini, Bahamas (25°44′N, 79°16′W). The sharks were observed to exhibit chafing, burst swimming, headshaking, resting and swimming in a semi-captive environment and these observations were used to ground-truth data for ML training and testing. ML methods included logistic regression, an artificial neural network, two random forest models, a gradient boosting model and a voting ensemble (VE) model, which combined the predictions of all other (base) models to improve classifier performance. The macro-averaged F-measure, an indicator of classifier performance, showed that the VE model improved overall classification (F-measure 0.88) above the strongest base learner model, gradient boosting (0.86). To test whether the VE model provided biologically meaningful results when applied to accelerometer data obtained from wild sharks, we investigated headshaking behaviour, as a proxy for prey capture, in relation to the variables: time of day, tidal phase and season. All variables were significant in predicting prey capture, with predations most likely to occur during early evening and less frequently during the dry season and high tides. These findings support previous hypotheses from sporadic visual observations.
Brewster, L. R., Dale, J. J., Guttridge, T. L., Gruber, S. H., Hansell, A. C., Elliott, M., …Gleiss, A. C. (2018). Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data. Marine Biology, 165(4), Article 62. https://doi.org/10.1007/s00227-018-3318-y
|Journal Article Type||Article|
|Acceptance Date||Jan 31, 2018|
|Online Publication Date||Mar 8, 2018|
|Publication Date||Apr 1, 2018|
|Deposit Date||May 31, 2022|
|Publicly Available Date||Oct 27, 2022|
|Peer Reviewed||Peer Reviewed|
Publisher Licence URL
© The Author(s) 2018. This article is an open access publication.<br /> Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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