A Baseline Model For Estimating the Risk of Gas Embolism in Sea Turtles During Routine Dives.

Sea turtles, like other air-breathing diving vertebrates, commonly experience significant gas embolism (GE) when incidentally caught at depth in fishing gear and brought to the surface. To better understand why sea turtles develop GE, we built a mathematical model to estimate partial pressures of N2 (PN2), O2 (PO2), and CO2 (PCO2) in the major body-compartments of diving loggerheads (Caretta caretta), leatherbacks (Dermochelys coriacea), and green turtles (Chelonia mydas). This model was adapted from a published model for estimating gas dynamics in marine mammals and penguins. To parameterize the sea turtle model, we used values gleaned from previously published literature and 22 necropsies. Next, we applied this model to data collected from free-roaming individuals of the three study species. Finally, we varied body-condition and cardiac output within the model to see how these factors affected the risk of GE. Our model suggests that cardiac output likely plays a significant role in the modulation of GE, especially in the deeper diving leatherback turtles. This baseline model also indicates that even during routine diving behavior, sea turtles are at high risk of GE. This likely means that turtles have additional behavioral, anatomical, and/or physiologic adaptions that serve to reduce the probability of GE but were not incorporated in this model. Identifying these adaptations and incorporating them into future iterations of this model will further reveal the factors driving GE in sea turtles.

Lipid profiling suggests species specificity and minimal seasonal variation in Pacific Green and Hawksbill Turtle plasma.

In this study, we applied multiple reaction monitoring (MRM)-profiling to explore the relative ion intensity of lipid classes in plasma samples from sea turtles in order to profile lipids relevant to sea turtle physiology and investigate how dynamic ocean environments affect these profiles. We collected plasma samples from foraging green (Chelonia mydas, n = 28) and hawksbill (Eretmochelys imbricata, n = 16) turtles live captured in North Pacific Costa Rica in 2017. From these samples, we identified 623 MRMs belonging to 10 lipid classes (sphingomyelin, phosphatidylcholine, free fatty acid, cholesteryl ester, phosphatidylserine, phosphatidylinositol, phosphatidylglycerol, phosphatidylethanolamine, ceramide, and triacylglyceride) and one metabolite group (acyl-carnitine) present in sea turtle plasma. The relative ion intensities of most lipids (80%) were consistent between species, across seasons, and were not correlated to body size or estimated sex. Of the differences we observed, the most pronounced was the differences in relative ion intensity between species. We identified 123 lipids that had species-specific relative ion intensities. While some of this variability is likely due to green and hawksbill turtles consuming different food items, we found indications of a phylogenetic component as well. Of these, we identified 47 lipids that varied by season, most belonging to the structural phospholipid classes. Overall, more lipids (n = 39) had higher relative ion intensity in the upwelling (colder) season compared to the non-upwelling season (n = 8). Further, we found more variability in hawksbill turtles than green turtles. Here, we provide the framework in which to apply future lipid profiling in the assessment of health, physiology, and behavior in endangered sea turtles.

Migratory corridor linking Atlantic green turtle, Chelonia mydas, nesting site on Bioko Island, Equatorial Guinea to Ghanaian foraging grounds.

This study uses satellite telemetry to track post-nesting movements of endangered green turtles (Chelonia mydas) (n = 6) in the Gulf of Guinea. It identifies a migratory corridor linking breeding grounds of Atlantic green turtles nesting on Bioko Island, Equatorial Guinea, to foraging grounds in the coastal waters of Accra, Ghana. Track lengths of 20-198 days were analyzed, for a total of 536 movement days for the six turtles. Migratory pathways and foraging grounds were identified by applying a switching state space model to locational data, which provides daily position estimates to identify shifts between migrating and foraging behavior. Turtles exhibited a combination of coastal and oceanic migrations pathways that ranged from 957 km to 1,131 km. Of the six turtles, five completed their migration and maintained residency at the same foraging ground near the coastal waters of Accra, Ghana until transmission was lost. These five resident turtles inhabit heavily fished waters and are vulnerable to a variety of anthropogenic threats. The identification of these foraging grounds highlights the importance of these coastal waters for the protection of the endangered Atlantic green turtle.