Predicting the distribution of green turtle nesting sites over the Mediterranean with outcoming climate driven changes

Abstract

Nesting beaches have a critical role in the life cycle of sea turtles and their survival. Many different factors affect nest site selection, ranging from the composition of the sand to the vegetation of the beach. These factors are subject to change due to the onset of climate change. We aimed to determine the possible changes in nesting beaches according to the future climate scenarios of Chelonia mydas nesting sites in the Mediterranean by ecological niche modeling. Nineteen bioclimatic variables and Representative Concentration Pathway scenarios (RCP2.6 and RCP8.5) were used to generate past, current, and future nesting site projections. The datasets were prepared with ArcGIS v10. and bioclimatic variables were analyzed using the Pearson Correlation Analysis. The ecological niche modeling was made with the MaxEnt v4.1.0. Model outputs, mean temperature of warmest quarter (22.01 %), precipitation of coldest quarter (15.32 %), mean temperature of the driest quarter (13.60 %), isothermality (12.30 %), mean diurnal range (9.22 %), the max temperature of the warmest month (6.60 %), precipitation seasonality (5.87 %) and annual mean temperature (4.73 %) are the parameters that most affect the estimated distribution of the species and the other parameters have the least effect on the estimated distribution (each < 2.60 %). The prediction accuracy of the model is measured by the Area Under the Curve (AUC) values, which is between 0 and 1, where values closer to 1 have a greater prediction accuracy. In our model results, the AUC values vary between 0.961 and 0.990. The majority of current green turtle nesting sites will continue to be suitable for nesting into the 2100′s. But the habitat suitability of the current nesting beaches in Syria and Lebanon will decrease. Conservational efforts should be developed to protect not only the current nesting beaches but also other possible nesting beaches that might become viable in the future.