Turtles in silico: Using computational fluid dynamics to mechanistically niche model leatherback sea turtles

To aid sea turtle conservation, academic literature and government publications state the need to predict future potential sea turtle nesting sites given different global warming scenarios. To accomplish this goal, managers will need to have a clear picture of how sea turtles respond to many combinations of climatic conditions across turtle migratory ranges and between terrestrial nesting sites and adjacent waters. Because global warming will create climatic combinations sea turtles do not currently encounter, assessing the sea turtle response is difficult and a mechanistic model may be the best approach. Our lab has successfully mechanistically niche mapped many terrestrial animals but not yet an aquatic species. As sea turtles are a marine species, the animal-fluid interactions make constructing a highly accurate mechanistic model complicated. The animal-fluid interaction not only affects the turtle's energy use (through thrust and drag) but also the heat transfer with its environment. To solve these issues we combine modern 3D design programs, computation fluid dynamic (CFD) software and in-house programs to construct a realistic, swimming leatherback sea turtle CFD simulation. These simulations allow us to analyze not only the animal-fluid interaction but also the turtle's internal heat transfer. We validate these models with data from the literature and flume and wind tunnel experiments. This simulation provides inputs for a mechanistic niche model, which can predict where leatherbacks can thermally persist both in water and on land. Thus with the niche model output, we can predict future potential nesting sites under different global warming scenarios. We show that global warming threatens leatherbacks with overheating, particularly in South East Asia. We also show that the impact may be less on leatherbacks that shift their nesting time or location or who are smaller. Methods such these are important to produce accurate maps of regions that will become inhospitable to species under global warming conditions.