TY - JOUR
T1 - Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution model
AU - Lopez, Jon
AU - Griffiths, Shane
AU - Wallace, Bryan P.
AU - Cáceres, Verónica
AU - Rodríguez, Luz Helena
AU - Abrego, Marino
AU - Alfaro-Shigueto, Joanna
AU - Andraka, Sandra
AU - Brito, María José
AU - Bustos, Leslie Camila
AU - Cari, Ilia
AU - Carvajal, José Miguel
AU - Clavijo, Ljubitza
AU - Cocas, Luis
AU - de Paz, Nelly
AU - Herrera, Marco
AU - Mangel, Jeffrey C.
AU - Pérez-Huaripata, Miguel
AU - Piedra, Rotney
AU - Dávila, Javier Antonio Quiñones
AU - Rendón, Liliana
AU - Rguez-Baron, Juan M.
AU - Santana, Heriberto
AU - Suárez, Jenifer
AU - Veelenturf, Callie
AU - Vega, Rodrigo
AU - Zárate, Patricia
N1 - Publisher Copyright:
© The authors 2024. Open Access under Creative Commons by Attribution Licence. Use, distribution and reproduction are unrestricted. Authors and original publication must be credited.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Boosted regression trees
KW - Conservation priority-setting
KW - Dermochelys coriacea
KW - Probability of occurrence
KW - Species distribution model
UR - http://www.scopus.com/inward/record.url?scp=85193002679&partnerID=8YFLogxK
U2 - 10.3354/ESR01288
DO - 10.3354/ESR01288
M3 - Artículo
AN - SCOPUS:85193002679
SN - 1863-5407
VL - 53
SP - 271
EP - 293
JO - Endangered Species Research
JF - Endangered Species Research
ER -