Whitehead DA, Magaña FG, Ketchum JT, Hoyos EM, Armas RG, Pancaldi F, Olivier D. The use of machine learning to detect foraging behaviour in whale sharks: a new tool in conservation.
J Fish Biol 2021;
98:865-869. [PMID:
33058201 DOI:
10.1111/jfb.14589]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 09/29/2020] [Accepted: 10/11/2020] [Indexed: 06/11/2023]
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.
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