Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution model

Jon Lopez, Shane Griffiths, Bryan P. Wallace, Verónica Cáceres, Luz Helena Rodríguez, Marino Abrego, Joanna Alfaro-Shigueto, Sandra Andraka, María José Brito, Leslie Camila Bustos, Ilia Cari, José Miguel Carvajal, Ljubitza Clavijo, Luis Cocas, Nelly de Paz, Marco Herrera, Jeffrey C. Mangel, Miguel Pérez-Huaripata, Rotney Piedra, Javier Antonio Quiñones DávilaLiliana Rendón, Juan M. Rguez-Baron, Heriberto Santana, Jenifer Suárez, Callie Veelenturf, Rodrigo Vega, Patricia Zárate

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 (>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 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 nongovernmental 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.

Original languageEnglish
Pages (from-to)271-293
Number of pages23
JournalEndangered Species Research
Volume53
DOIs
StatePublished - 2024

Keywords

  • Boosted regression trees
  • Conservation priority-setting
  • Dermochelys coriacea
  • Probability of occurrence
  • Species distribution model

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