Centre d'Étude et de Découverte des Tortues Marines

Patterns of mitochondrial DNA (mtDNA) variation were used to analyse the population genetic structure of southwestern Indian Ocean green turtle (Chelonia mydas) populations. Analysis of sequence variation over 396 bp of the mtDNA control region revealed seven haplotypes among 288 individuals from 10 nesting sites in the Southwest Indian Ocean. This is the first time that Atlantic Ocean haplotypes have been recorded among any Indo-Pacific nesting populations. Previous studies indicated that the Cape of Good Hope was a major biogeographical barrier between the Atlantic and Indian Oceans because evidence for gene flow in the last 1.5 million years has yet to emerge. This study, by sampling localities adjacent to this barrier, demonstrates that recent gene flow has occurred from the Atlantic Ocean into the Indian Ocean via the Cape of Good Hope. We also found compelling genetic evidence that green turtles nesting at the rookeries of the South Mozambique Channel (SMC) and those nesting in the North Mozambique Channel (NMC) belong to separate genetic stocks. Furthermore, the SMC could be subdivided in two different genetic stocks, one in Europa and the other one in Juan de Nova. We suggest that this particular genetic pattern along the Mozambique Channel is attributable to a recent colonization from the Atlantic Ocean and is maintained by oceanic conditions in the northern and southern Mozambique Channel that influence early stages in the green turtle life cycle.

BACKGROUND: A strong behavioural plasticity is commonly evidenced in the movements of marine megafauna species, and it might be related to an adaptation to local conditions of the habitat. One way to investigate such behavioural plasticity is to satellite track a large number of individuals from contrasting foraging grounds, but despite recent advances in satellite telemetry techniques, such studies are still very limited in sea turtles. METHODS: From 2010 to 2018, 49 juvenile green turtles were satellite tracked from five contrasting feeding grounds located in the South-West Indian Ocean in order to (1) assess the diel patterns in their movements, (2) investigate the inter-individual and inter-site variability, and (3) explore the drivers of their daily movements using both static (habitat type and bathymetry) and dynamic variables (daily and tidal cycles). RESULTS: Despite similarities observed in four feeding grounds (a diel pattern with a decreased distance to shore and smaller home ranges at night), contrasted habitats (e.g. mangrove, reef flat, fore-reef, terrace) associated with different resources (coral, seagrass, algae) were used in each island. CONCLUSIONS: Juvenile green turtles in the South-West Indian Ocean show different responses to contrasting environmental conditions - both natural (habitat type and tidal cycle) and anthropogenic (urbanised vs. uninhabited island) demonstrating the ability to adapt to modification of habitat.

Age is a fundamental life history attribute that is used to understand the dynamics of wild animal populations. Unfortunately, most animals do not have a practical or nonlethal method to determine age. This makes it difficult for wildlife managers to carry out population assessments, particularly for elusive and long-lived fauna such as marine turtles. In this study, we present an epigenetic clock that predicts the age of marine turtles from skin biopsies. The model was developed and validated using DNA from known-age green turtles (Chelonia mydas) from two captive populations, and mark-recapture wild turtles with known time intervals between captures. Our method, based on DNA methylation levels at 18 CpG sites, was highly accurate with a median absolute error of 2.1 years (4.7% of maximum age in data set). This is the first epigenetic clock developed for a reptile and illustrates their broad applicability across a broad variety of vertebrate species. It has the potential to transform marine turtle management through a nonlethal and inexpensive method to provide key life history information.

Monitoring the abundance of green turtles (Chelonia mydas) is necessary to assess population trends and risks of collapse. This note presents a study aimed at comparing three techniques for the direct estimation of green turtle numbers in their foraging habitats (seagrass beds and reef flats). The experiment was carried out at Mayotte Island, Western Indian Ocean. The techniques involved were surveys by snorkel, and aerial surveys using a microlight aircraft and a paramotor. Each technique had shortcomings and advantages. While each technique provided estimations of turtle numbers only surveys by snorkel permitted identification of species and sex, whenever visibility and turtle behaviour permitted. Along the shorelines, and over foraging areas, the paramotor was found to be most suitable for direct estimations of turtle numbers. The major advantage of this technique lied in its capability to obtain a synoptic snapshot of turtle distribution over foraging areas. Linear surveys from a microlight aircraft are better suited to monitor foraging areas located further away from the shore.

Understanding how ocean currents impact the distribution and connectivity of marine species, provides vital information for the effective conservation management of migratory marine animals. Here, we used a combination of molecular genetics and ocean drift simulations to investigate the spatial ecology of juvenile green turtle (Chelonia mydas) developmental habitats, and assess the role of ocean currents in driving the dispersal of green turtle hatchlings. We analyzed mitochondrial (mt)DNA sequenced from 358 juvenile green turtles, and from eight developmental areas located throughout the Southwest Indian Ocean (SWIO). A mixed stock analysis (MSA) was applied to estimate the level of connectivity between developmental sites and published genetic data from 38 known genetic stocks. The MSA showed that the juvenile turtles at all sites originated almost exclusively from the three known SWIO stocks, with a clear shift in stock contributions between sites in the South and Central Areas. The results from the genetic analysis could largely be explained by regional current patterns, as shown by the results of passive numerical drift simulations linking breeding sites to developmental areas utilized by juvenile green turtles. Integrating genetic and oceanographic data helps researchers to better understand how marine species interact with ocean currents at different stages of their lifecycle, and provides the scientific basis for effective conservation management.

, migrate between foraging and nesting sites that are generally separated by thousands of kilometers. As an emblematic endangered species, green turtles have been intensively studied, with a focus on nesting, migration, and foraging. Nevertheless, few attempts integrated these behaviors and their trade-offs by considering the spatial configurations of foraging and nesting grounds as well as environmental heterogeneity like oceanic currents and food distribution. We developed an individual-based model to investigate the impact of local environmental conditions on emerging migratory corridors and reproductive output and to thereby identify conservation priority sites. The model integrates movement, nesting, and foraging behavior. Despite being largely conceptual, the model captured realistic movement patterns which confirm field studies. The spatial distribution of migratory corridors and foraging hot spots was mostly constrained by features of the regional landscape, such as nesting site locations, distribution of feeding patches, and oceanic currents. These constraints also explained the mixing patterns in regional forager communities. By implementing alternative decision strategies of the turtles, we found that foraging site fidelity and nesting investment, two characteristics of green turtles' biology, are favorable strategies under unpredictable environmental conditions affecting their habitats. Based on our results, we propose specific guidelines for the regional conservation of green turtles as well as future research suggestions advancing spatial ecology of sea turtles. Being implemented in an easy to learn open-source software, our model can coevolve with the collection and analysis of new data on energy budget and movement into a generic tool for sea turtle research and conservation. Our modeling approach could also be useful for supporting the conservation of other migratory marine animals.

Despite the large number of species distribution modelling (SDM) applications driven by tracking data, individual information is most of the time neglected and traditional SDM approaches commonly focus on predicting the potential distribution at the species or population‐level. By running classical SDMs (population approach) with mixed models including a random factor to account for the variability attributable to individual (individual approach), we propose an innovative five‐steps framework to predict the potential and individual‐level distributions of mobile species using GPS data collected from green turtles. Pseudo‐absences were randomly generated following an environmentally‐stratified procedure. A negative exponential dispersal kernel was incorporated into the individual model to account for spatial fidelity, while five environmental variables derived from high‐resolution Lidar and hyperspectral data were used as predictors of the species distribution in generalized linear models. Both approaches showed a strong predictive power (mean: AUC > 0.93, CBI > 0.88) and goodness‐of‐fit (0.6 < adjusted R 2 < 0.9), but differed geographically with favorable habitats restricted around the tagging locations for the individual approach whereas favorable habitats from the population approach were more widespread. Our innovative way to combine predictions from both approaches into a single map provides a unique scientific baseline to support conservation planning and management of many taxa. Our framework is easy to implement and brings new opportunities to exploit existing tracking dataset, while addressing key ecological questions such as inter‐individual plasticity and social interactions.

Surface and sub-surface ocean temperature observations collected by sea turtles (ST) during the first phase (Jan-2019 – April 2020) of the Sea Turtle for Ocean Research and Monitoring (STORM) program are compared against in-situ and satellite temperature measurements, and later relied upon to assess the performance of Glo12 ocean model forecasts over the west tropical Indian Ocean. The evaluation of ocean temperature profiles collected by STs against collocated ARGO drifter measurements show good agreement, with imperceptible discrepancies at all sample depths (0-250m). Comparisons against various operational satellite sea surface temperature (SST) products indicate a slight overestimation of ST-borne temperature observations of ~ 0.1° +/- 0.6° that is consistent with expected uncertainties on satellite derived SST data. Comparisons of ST-borne surface and subsurface temperature observations against Glo12 operational ocean model forecasts demonstrate the good performance of modelled surface and subsurface (<50m) temperature predictions in the West tropical Indian Ocean, with mean bias (resp. RMS) in the range of 0.2° (resp. 0.5 - 1.5°). At deeper depths (>50m), the model is however shown to significantly underestimate ocean temperatures as already noticed from global evaluation scores performed operationally at the basin scale. The distribution of model errors also shows significant spatial and temporal variability in the first 50 m of the ocean, which will be further investigated in the next phases of the STORM project.

While scientists have been monitoring the movements and diving behaviour of sea turtles using Argos platform terminal transmitters for decades, the precise navigational mechanisms used by these animals remain an open question. Until now, active swimming motion has been derived from total motion by subtracting surface or subsurface modelled ocean currents, following the approximation of a quasi-two-dimensional surface layer migration. This study, based on tracking and diving data collected from 25 late-juvenile loggerhead turtles released from Reunion Island during their pre-reproductive migration, demonstrates the importance of considering the subsurface presence of the animals. Using a piecewise constant heading model, we investigate navigation strategy using daily time-at-depth distributions and three-dimensional currents to calculate swimming velocity. Our results are consistent with a map and compass strategy in which swimming movements follow straight courses at a stable swimming speed (approx. 0.5 m s −1 ), intermittently segmented by course corrections. This strategy, previously hypothesized for post-nesting green and hawksbill turtles, had never been observed in juvenile loggerheads. These results confirm a common open-ocean navigation mechanism across ages and species and highlight the importance of considering diving behaviour in most studies of sea turtle spatial ecology.

In marine turtles, the sex of hatchlings is determined by their egg incubation temperature. Global warming may increase the extinction risk by skewing hatchling sex ratios. Assessment of this risk at the population level requires the identification of sex in hatchlings and juveniles. However, available methods are typically lethal, highly invasive, or difficult to conduct at a large scale. Changes in DNA methylation, an epigenetic modification, have been characterized as part of sex differentiation pathways in some species with environmentally determined sex, but so far not in marine turtles. Neither have epigenetic biomarkers for sex been developed into rapid assays suited to research on wildlife. In this study, we aimed to develop a rapid, minimally invasive, and inexpensive method to identify the sex of marine turtles. We used reduced representation bisulfite sequencing DNA methylation data from adult green sea turtle ( Chelonia mydas ) skin biopsies to identify 16 genomic regions exhibiting differential methylation between males and females (adjusted p-value < 0.01). We designed methylation sensitive qPCR assays for these regions and tested their capacity to identify the sex of turtles ranging in age between 3-34 years. The qPCR assay identified the correct sex in turtles > 17 years. However, the sex of younger turtles could not be accurately identified. This suggests the sex differences distinguishable by the assay were adult specific, reflecting the training data on which the sex-specific regions were identified, and likely linked to late-stage ontogenetic changes associated with sexual maturity. Epigenetic biomarkers are a promising tool for wildlife research because they can be minimally invasive and high throughput. Future research into sex-specific differentially methylated regions in hatchlings and juveniles should be based on genome-wide DNA methylation data from a wider age range, which includes hatchlings.

Loggerhead sea turtles ( Caretta caretta ) use both oceanic and neritic habitats depending on their life stage, eventually undertaking an ontogenetic shift. Juveniles likely start foraging in a purely opportunistic manner and later seek resources more actively. In the Indian Ocean, it is still unclear where oceanic-stage individuals go, what they do, and importantly where they forage. Yet, such information is crucial to protect this endangered species from anthropogenic threats such as bycatch in fisheries. To address this, 67 individuals (66 late juveniles and one adult) bycaught in the open ocean were equipped with satellite tags and released in the Southwestern Indian Ocean between 2008 and 2021. Most individuals traveled to the Northwestern Indian Ocean where they used neritic habitats of the continental shelf (i.e., largely between 0 and 200-m depth). Using hidden Markov models, we identified three types of movements likely associated with traveling, wandering, and foraging behaviors. We found that the movement characteristics of these behaviors differ depending on turtles’ target destination and habitat (oceanic vs neritic), highlighting different strategies of habitat use among individuals of presumably the same life stage (late juveniles). The turtles that traveled to the Northwestern Indian Ocean encountered warmer waters (mean = 27.6°C, min. = 20.6°C, max. = 33.1°C) than their counterparts remaining in the Southern Hemisphere (mean = 22.5°C, min. = 14.6°C, max. = 29.7°C) but were found foraging at locations with comparable biomass of potential prey (mean = 2.5 g C m -2 , min. = 0.5 g C m -2 , max. = 10.4 g C m -2 ) once in the Northern Hemisphere. It remains obscure why these individuals undertook a trans-equatorial migration. Once in neritic habitats, the proportion of time spent traveling was considerably reduced (from 33% to 19%) and allocated to foraging instead. In light of this, it is very likely that the individuals migrated to the Northwestern Indian Ocean to undergo an oceanic-to-neritic ontogenetic shift. Our study sheds light on the behavioral ecology of loggerhead turtles and identifies important foraging areas in the Western Indian Ocean, with the top-three most densely used ones being the Gulf of Oman, the Central Somali Coast, and the Western Arabian Sea.

International audience

Stage encadré par : Quentin Rivière (maître de stage) et Marie Patry (tuteur de stage)

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Objectifs : La conservation des herbiers de phanérogames marines comme support de la biodiversité et du maintien des populations de tortues marines au sein du Parc.OBJ.1 Améliorer les connaissances sur les herbiers et leurs rôles fonctionnels pour les tortues marinesOBJ.2 Initier un réseau régional de suivi des herbiers OBJ.3 Renforcer la gestion du Parc pour une plus grande protection des herbiers

International audience

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