Résumé
The identification of sea turtle behaviours is a prerequisite to
predicting the activities and time-budget of these animals in their
natural habitat over 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 behaviours that
are of crucial ecological interest for sea turtles, such as feeding and
scratching. Our procedure uses multi-sensor miniaturised 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.