ESR 53:271-293 (2024) - DOI: https://doi.org/10.3354/esr01288
Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution model
Jon Lopez1,*, Shane Griffiths1, Bryan P. Wallace2,3,4,*, Verónica Cáceres4, Luz Helena Rodríguez4, Marino Abrego5, Joanna Alfaro-Shigueto6,7,8, Sandra Andraka9, María José Brito10, Leslie Camila Bustos11, Ilia Cari12, José Miguel Carvajal13, Ljubitza Clavijo12, Luis Cocas11, Nelly de Paz14, Marco Herrera10, Jeffrey C. Mangel7,8, Miguel Pérez-Huaripata15, Rotney Piedra16, Javier Antonio Quiñones Dávila15, Liliana Rendón9, Juan M. Rguez-Baron17,18, Heriberto Santana19, Jenifer Suárez20, Callie Veelenturf21, Rodrigo Vega12, Patricia Zárate12
ABSTRACT: The Eastern Pacific 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 = 1088) and absence data (>500000 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 obtained through a participatory collaboration between the Inter-American Convention for the Protection and Conservation of Sea Turtles and the Inter-American Tropical Tuna Commission as well as non-governmental organizations to support the quantification of leatherback vulnerability to fisheries bycatch. A daily process was applied to predict the 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 (Griffiths & Wallace et al. 2024; Endang Species Res 53:295-326). In addition, this approach can provide a modeling framework for other data-limited vulnerable populations and species.
KEY WORDS: Dermochelys coriacea · Species distribution model · Probability of occurrence · Boosted regression trees · Conservation priority-setting
Full text in pdf format  Supplementary material | Cite this article as: Lopez J, Griffiths S, Wallace BP, Cáceres V and others (2024) Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution model. Endang Species Res 53:271-293. https://doi.org/10.3354/esr01288
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