1. Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data
- Author
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Brewster, L.R., Dale, J.J., Guttridge, T.L., Gruber, S.H., Hansell, A.C., Elliott, M., Cowx, I.G., Whitney, N.M., and Gleiss, A.C.
- Subjects
Animal spatial behavior -- Observations ,Machine learning -- Methods ,Lemon shark -- Observations ,Accelerometers -- Usage ,Biological sciences - Abstract
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[degrees]44'N, 79[degrees]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., Introduction Selecting the optimal behavioural response can increase individual fitness, have adaptive significance and evolutionary consequences (Lima and Dill 1990; McNamara and Houston 1996; Shepard et al. 2008b). Identification of [...]
- Published
- 2018
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