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.

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Predictive modelling to map subtidal communities is an alternative to "traditional" methods, such as direct sampling, remote sensing and acoustic survey, which are neither time-nor cost-effective for vast expanses. The principle of this modelling is the use of a combination of environmental key parameters to produce rules to understand species distribution and hence generate predictive maps. This study focuses on subtidal kelp forests (KF) on the coast of Brittany, France. The most significant key parameters to predict KF frequency are (1) the nature of the substrate, (2) depth, (3) water transparency, (4) water surface temperature and (5) hydrodynamics associated with the flexibility of algae in a flow. All these parameters are integrated in a spatial model, built using a Geographical Information System. This model results in a KF frequency map, where sites with optimum key parameters show a deeper limit of disappearance. After validation, the model is used in the context of Climate Change to estimate the effect of environmental variation on this depth limit of KF. Thus, the effects of both an increase in water temperature and a decrease in its transparency could lead to the complete disappearance of KF.

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