Journal article
Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data
Marine Biology, Vol.165(4), Article 62
2018
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°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.
Details
- Title
- Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data
- Authors/Creators
- L.R. Brewster (Author/Creator) - Bimini Biological Field Station FoundationJ.J. Dale (Author/Creator) - Stanford UniversityT.L. Guttridge (Author/Creator) - Bimini Biological Field Station FoundationS.H. Gruber (Author/Creator) - Cooperative Institute for Marine and Atmospheric StudiesA.C. Hansell (Author/Creator) - University of Massachusetts DartmouthM. Elliott (Author/Creator) - University of HullI.G. Cowx (Author/Creator) - University of HullN.M. Whitney (Author/Creator) - New England AquariumA.C. Gleiss (Author/Creator) - Murdoch University
- Publication Details
- Marine Biology, Vol.165(4), Article 62
- Identifiers
- 991005543591107891
- Murdoch Affiliation
- School of Veterinary and Life Sciences
- 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
- Marine & Freshwater Biology
- ESI research areas
- Plant & Animal Science