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.

The change of animal biometrics (body mass and body size) can reveal important information about their living environment as well as determine the survival potential and reproductive success of individuals and thus the persistence of populations. However, weighing individuals like marine turtles in the field presents important logistical difficulties. In this context, estimating body mass (BM) based on body size is a crucial issue. Furthermore, the determinants of the variability of the parameters for this relationship can provide information about the quality of the environment and the manner in which individuals exploit the available resources. This is of particular importance in young individuals where growth quality might be a determinant of adult fitness. Our study aimed to validate the use of different body measurements to estimate BM, which can be difficult to obtain in the field, and explore the determinants of the relationship between BM and size in juvenile green turtles. Juvenile green turtles were caught, measured, and weighed over 6 years (2011–2012; 2015–2018) at six bays to the west of Martinique Island (Lesser Antilles). Using different datasets from this global database, we were able to show that the BM of individuals can be predicted from body measurements with an error of less than 2%. We built several datasets including different morphological and time-location information to test the accuracy of the mass prediction. We show a yearly and north–south pattern for the relationship between BM and body measurements. The year effect for the relationship of BM and size is strongly correlated with net primary production but not with sea surface temperature or cyclonic events. We also found that if the bay locations and year effects were removed from the analysis, the mass prediction degraded slightly but was still less than 3% on average. Further investigations of the feeding habitats in Martinique turtles are still needed to better understand these effects and to link them with geographic and oceanographic conditions.