A machine learning species distribution model for the critically endangered East Pacific leatherback turtle Dermochelys coriacea

An early version of this paper was published as IATTC-2022-BYC-11-01 https://www.bmis-bycatch.org/references/x65qdjprFind it here:

The Eastern Pacific (EP) population of leatherback turtles (Dermochelys coriacea) is critically endangered, with incidental capture in coastal and pelagic fisheries as one of the major causes. Given the population’s broad geographic range, status, and extensive overlap with fisheries throughout the region, identifying areas of high importance is essential for effective conservation and management. In this study, we created a machine learning species distribution model trained with remotely sensed environmental data and fishery-dependent leatherback presence (n=1,088) and absence data (>500,000 fishing sets with no turtle observations) from industrial and small-scale fisheries that operated in the eastern Pacific Ocean between 1995 and 2020. The data were contributed through a participatory collaboration between the Inter-American Convention for the Protection and Conservation of Sea Turtles (IAC) and the Inter-American Tropical Tuna Commission (IATTC), as well as non-governmental organizations, to support quantification of leatherback vulnerability to fisheries bycatch. A daily process was applied to predict probability of leatherback occurrence as a function of dynamic and static environmental covariates. Coastal areas throughout the region were highlighted as important habitats, particularly highly productive feeding areas over the continental shelf of Ecuador, Peru, and offshore from Chile and breeding areas off Mexico and Central America. Our model served as the basis to quantify leatherback vulnerability to fisheries bycatch and the potential efficacy of conservation and management measures presented in a companion paper. In addition, this model can provide a modeling framework for other data-limited vulnerable populations and species.