Movement ecology studies are essential to protect highly mobile threatened species such as the green turtle (Chelonia mydas), classified as an endangered species by the IUCN. In 2019, the South Atlantic subpopulation has been downlisted to 'Least Concern', but the maintenance of this status strongly relies on the pursuit of research and conservation, especially on immatures, which contribute to the demographic renewal of this subpopulation. Identifying marine areas used by immatures is therefore crucial to implement efficient measures for the conservation of sea turtles in the Caribbean. We analysed data of capture-mark-recapture of 107 (out of 299) immatures recaptured at least once in Martinique, and satellite tracked 24 immatures to investigate their site fidelity and habitat use. Our results revealed a strong fidelity to foraging grounds, with mean residence times higher than 2 years, and with a high degree of affinity for specific areas within the coastal marine vegetation strip. Home ranges (95% kernel contour) and core areas (50% kernel contour) varied from 0.17 to 235.13 km 2 (mean ± SD = 30.73 ± 54.34 km 2) and from 0.03 to 22.66 km 2 (mean ± SD = 2.95 ± 5.06 km 2), respectively. Our findings shed light on a critical developmental area for immature green turtles in the French West Indies, and should help to refine Regional Management Units and reinforce the cooperative network aiming at ensuring conservation of the species at international scale.
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.
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 (), an adult hawksbill () and an adult green turtle () 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.
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