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J Exp Biol. 2018 May 23;221(Pt 10). pii: jeb177378. doi: 10.1242/jeb.177378.

Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data.

Author information

1
DEPE-IPHC, UMR 7178, CNRS, 23 rue Becquerel, 67087 Strasbourg cedex 2, France lorene.jeantet@iphc.cnrs.fr.
2
Aquarium La Rochelle, quai Louis Prunier, 17000 La Rochelle, France.
3
DEPE-IPHC, UMR 7178, CNRS, 23 rue Becquerel, 67087 Strasbourg cedex 2, France.
4
Direction de l'Environnement, de l'Aménagement et du Logement Martinique, BP 7217, 97274 Schoelcher cedex, Martinique.
5
Office de l'Eau Martinique, 7 avenue Condorcet, BP 32, 97201 Fort-de-France, Martinique.
6
Institut Pasteur de la Guyane, 23 avenue Pasteur, BP 6010, Cayenne cedex, Guyane.
7
Biological Sciences, Institute of Environmental Sustainability, Swansea University, Swansea SA2 8PP, UK.

Abstract

Accelerometers are becoming ever more important sensors in animal-attached technology, providing data that allow determination of body posture and movement and thereby helping to elucidate behaviour in animals that are difficult to observe. We sought to validate the identification of sea turtle behaviours from accelerometer signals by deploying tags on the carapace of a juvenile loggerhead (Caretta caretta), an adult hawksbill (Eretmochelys imbricata) and an adult green turtle (Chelonia mydas) at Aquarium La Rochelle, France. We recorded tri-axial acceleration at 50 Hz for each species for a full day while two fixed cameras recorded their behaviours. We identified behaviours from the acceleration data using two different supervised learning algorithms, Random Forest and Classification And Regression Tree (CART), treating the data from the adult animals as separate from the juvenile data. We achieved a global accuracy of 81.30% for the adult hawksbill and green turtle CART model and 71.63% for the juvenile loggerhead, identifying 10 and 12 different behaviours, respectively. Equivalent figures were 86.96% for the adult hawksbill and green turtle Random Forest model and 79.49% for the juvenile loggerhead, for the same behaviours. The use of Random Forest combined with CART algorithms allowed us to understand the decision rules implicated in behaviour discrimination, and thus remove or group together some 'confused' or under--represented behaviours in order to get the most accurate models. This study is the first to validate accelerometer data to identify turtle behaviours and the approach can now be tested on other captive sea turtle species.

KEYWORDS:

Accelerometry; Endangered species; Supervised learning algorithms

PMID:
29661804
DOI:
10.1242/jeb.177378

Conflict of interest statement

Competing interestsThe authors declare no competing or financial interests.

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