These limitations have, in part, catalysed the development of ‘biotelemetry’, where study animals wear systems that transmit data to remote receiving stations ( Cagnacci et al., 2010 Tomkiewicz et al., 2010). In fact, direct observation is often considered impossible in far-ranging, shy or nocturnal species. The study of animal behaviour has historically been limited by the difficulty of observing individuals in the wild, and also because human presence can change animal behaviour ( Schneirla, 1950 Jack et al., 2008). 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. 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. 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. 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. 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 recorded tri-axial acceleration at 50 Hz for each species for a full day while two fixed cameras recorded their behaviours. ![]() 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. 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.
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