The use of machine learning to detect foraging behaviour in whale sharks: a new tool in conservation
Darren A. Whitehead, Felipe Galván‐Magaña, James T. Ketchum, Edgar M. Hoyos, Rogelio González‐Armas, Francesca Pancaldi, Damien Olivier
Abstract
In this study we present the first attempt at modelling the feeding behaviour of whale sharks using a machine learning analytical method. A total of eight sharks were monitored with tri-axial accelerometers and their foraging behaviours were visually observed. Our results highlight that the random forest model is a valid and robust approach to predict the feeding behaviour of the whale shark. In conclusion this novel approach exposes the practicality of this method to serve as a conservation tool and the capability it offers in monitoring potential disturbances of the species.
Topics & Concepts
ForagingWhaleBiologyAccelerometerFisheryCetaceaRandom forestMachine learningArtificial intelligenceEcologyComputer scienceOperating systemIchthyology and Marine BiologyFish Ecology and Management StudiesUnderwater Acoustics Research