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ESR 53:271-293 (2024)  -  DOI:

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

1Inter-American Tropical Tuna Commission, 8901 La Jolla Shores Drive, La Jolla, CA 92037, USA
2Ecolibrium, Inc., 5343 Aztec Drive, Boulder, CO 80303, USA
3Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80310, USA
4Inter-American Convention for the Protection and Conservation of Sea Turtles, Falls Church, VA 22046, USA
5Ministerio de Ambiente, Panama City C-0843-00793, Panamá
6Carrera de Biologia Marina, Universidad Cientifica del Sur, Lima 15067 Perú
7ProDelphinus, Jose Galvez 780E, Lima 10680 Perú
8School of Biosciences, University of Exeter, Cornwall Campus, Penryn, Cornwall TR10 9EZ, UK
9EcoPacifico+, San José 11801, Costa Rica
10Instituto Público de Investigación de Acuicultura y Pesca, Guayaquil 090314, Ecuador
11Subsecretaría de Pesca y Acuicultura, Valparaíso 2340000, Chile
12Instituto de Fomento Pesquero, Valparaíso 2340000, Chile
13Instituto Nacional Costarricense de Pesca y Acuicultura, Puntarenas 60101, Costa Rica
14Áreas Costeras y Recursos Marinos, Pisco11600, Perú
15Instituto del Mar del Perú, Callao 07021, Peru
16Sistema Nacional de Áreas de Conservación, Nicoya 50201, Costa Rica
17JUSTSEA Foundation, Bogotá 1100111, Colombia
18University of North Carolina Wilmington, Wilmington, NC 28403, USA
19Instituto National de Pesca y Acuacultura, Manzanillo, Colima 28200, Mexico
20Parque Nacional Galápagos, Puerto Ayora, Galápagos Islands 200101, Ecuador
21The Leatherback Project, Norfolk, MA 02056, USA
*Corresponding authors:

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

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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.

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