Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology. Issue 5 (13th May 2020)

• Record Type: Journal Article
• Authors: Jeantet, Lorène; Planas-Bielsa, Víctor; Benhamou, Simon; Geiger, Sebastien; Martin, Jordan; Siegwalt, Flora; Lelong, Pierre; Gresser, Julie; Etienne, Denis; Hiélard, Gaëlle; Arque, Alexandre; Regis, Sidney; Lecerf, Nicolas; Frouin, Cédric; Benhalilou, Abdelwahab; Murgale, Céline; Maillet, Thomas; Andreani, Lucas; Campistron, Guilhem; Delvaux, Hélène; Guyon, Christelle; Richard, Sandrine; Lefebvre, Fabien; Aubert, Nathalie; Habold, Caroline; le Maho, Yvon; Chevallier, Damien
• Is Part Of: Royal Society open science. Volume 7:Issue 5(2020)
• Publication Date: 2020-05-13
• Subjects: supervised learning algorithms -- accelerometer -- sea turtle -- animal-borne camera -- behavioural classification -- marine ecology; Science -- Periodicals; 500
• DOI: 10.1098/rsos.200139
• Languages: English
• ISSNs: 2054-5703

Abstract

Abstract : The identification of sea turtle behaviours is a prerequisite to predicting the activities and time-budget of these animals in their natural habitat over the long term. However, this is hampered by a lack of reliable methods that enable the detection and monitoring of certain key behaviours such as feeding. This study proposes a combined approach that automatically identifies the different behaviours of free-ranging sea turtles through the use of animal-borne multi-sensor recorders (accelerometer, gyroscope and time-depth recorder), validated by animal-borne video-recorder data. We show here that the combination of supervised learning algorithms and multi-signal analysis tools can provide accurate inferences of the behaviours expressed, including feeding and scratching behaviours that are of crucial ecological interest for sea turtles. Our procedure uses multi-sensor miniaturized loggers that can be deployed on free-ranging animals with minimal disturbance. It provides an easily adaptable and replicable approach for the long-term automatic identification of the different activities and determination of time-budgets in sea turtles. This approach should also be applicable to a broad range of other species and could significantly contribute to the conservation of endangered species by providing detailed knowledge of key animal activities such as feeding, travelling and resting.