Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution model
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,...
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| Format: | Article |
| Language: | English |
| Published: |
Inter-Research
2024-03-01
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| Series: | Endangered Species Research |
| Online Access: | https://www.int-res.com/abstracts/esr/v53/p271-293/ |
| _version_ | 1797214488853741568 |
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| author | J Lopez S Griffiths BP Wallace V Cáceres L Helena Rodríguez M Abrego J Alfaro-Shigueto S Andraka M José Brito L Camila Bustos I Cari JM Carvajal L Clavijo L Cocas N de Paz M Herrera JC Mangel M Pérez-Huaripata R Piedra JA Quiñones Dávila L Rendón JM Rguez-Baron H Santana J Suárez C Veelenturf R Vega P Zárate |
| author_facet | J Lopez S Griffiths BP Wallace V Cáceres L Helena Rodríguez M Abrego J Alfaro-Shigueto S Andraka M José Brito L Camila Bustos I Cari JM Carvajal L Clavijo L Cocas N de Paz M Herrera JC Mangel M Pérez-Huaripata R Piedra JA Quiñones Dávila L Rendón JM Rguez-Baron H Santana J Suárez C Veelenturf R Vega P Zárate |
| author_sort | J Lopez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T11:14:59Z |
| format | Article |
| id | doaj.art-7639a05cde994655b6b68e8d5057321a |
| institution | Directory Open Access Journal |
| issn | 1863-5407 1613-4796 |
| language | English |
| last_indexed | 2024-04-24T11:14:59Z |
| publishDate | 2024-03-01 |
| publisher | Inter-Research |
| record_format | Article |
| series | Endangered Species Research |
| spelling | doaj.art-7639a05cde994655b6b68e8d5057321a2024-04-11T09:52:02ZengInter-ResearchEndangered Species Research1863-54071613-47962024-03-015327129310.3354/esr01288Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution modelJ Lopez0S Griffiths1BP Wallace2V Cáceres3L Helena Rodríguez4M Abrego5J Alfaro-Shigueto6S Andraka7M José Brito8L Camila Bustos9I Cari10JM Carvajal11L Clavijo12L Cocas13N de Paz14M Herrera15JC Mangel16M Pérez-Huaripata17R Piedra18JA Quiñones Dávila19L Rendón20JM Rguez-Baron21H Santana22J Suárez23C Veelenturf24R Vega25P Zárate26Inter-American Tropical Tuna Commission, 8901 La Jolla Shores Drive, La Jolla, CA 92037, USAInter-American Tropical Tuna Commission, 8901 La Jolla Shores Drive, La Jolla, CA 92037, USAEcolibrium, Inc., 5343 Aztec Drive, Boulder, CO 80303, USAInter-American Convention for the Protection and Conservation of Sea Turtles, Falls Church, VA 22046, USAInter-American Convention for the Protection and Conservation of Sea Turtles, Falls Church, VA 22046, USAMinisterio de Ambiente, Panama City C-0843-00793, PanamáCarrera de Biologia Marina, Universidad Cientifica del Sur, Lima 15067 PerúEcoPacifico+, San José 11801, Costa RicaInstituto Público de Investigación de Acuicultura y Pesca, Guayaquil 090314, EcuadorSubsecretaría de Pesca y Acuicultura, Valparaíso 2340000, ChileInstituto de Fomento Pesquero, Valparaíso 2340000, ChileInstituto Nacional Costarricense de Pesca y Acuicultura, Puntarenas 60101, Costa RicaInstituto de Fomento Pesquero, Valparaíso 2340000, ChileSubsecretaría de Pesca y Acuicultura, Valparaíso 2340000, ChileÁreas Costeras y Recursos Marinos, Pisco11600, PerúInstituto Público de Investigación de Acuicultura y Pesca, Guayaquil 090314, EcuadorProDelphinus, Jose Galvez 780E, Lima 10680 PerúInstituto del Mar del Perú, Callao 07021, PeruSistema Nacional de Áreas de Conservación, Nicoya 50201, Costa RicaInstituto del Mar del Perú, Callao 07021, PeruEcoPacifico+, San José 11801, Costa RicaJUSTSEA Foundation, Bogotá 1100111, ColombiaInstituto National de Pesca y Acuacultura, Manzanillo, Colima 28200, MexicoParque Nacional Galápagos, Puerto Ayora, Galápagos Islands 200101, EcuadorThe Leatherback Project, Norfolk, MA 02056, USAInstituto de Fomento Pesquero, Valparaíso 2340000, ChileInstituto de Fomento Pesquero, Valparaíso 2340000, ChileThe 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.https://www.int-res.com/abstracts/esr/v53/p271-293/ |
| spellingShingle | J Lopez S Griffiths BP Wallace V Cáceres L Helena Rodríguez M Abrego J Alfaro-Shigueto S Andraka M José Brito L Camila Bustos I Cari JM Carvajal L Clavijo L Cocas N de Paz M Herrera JC Mangel M Pérez-Huaripata R Piedra JA Quiñones Dávila L Rendón JM Rguez-Baron H Santana J Suárez C Veelenturf R Vega P Zárate Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution model Endangered Species Research |
| title | Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution model |
| title_full | Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution model |
| title_fullStr | Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution model |
| title_full_unstemmed | Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution model |
| title_short | Vulnerability of the Critically Endangered leatherback turtle to fisheries bycatch in the eastern Pacific Ocean. I. A machine-learning species distribution model |
| title_sort | vulnerability of the critically endangered leatherback turtle to fisheries bycatch in the eastern pacific ocean i a machine learning species distribution model |
| url | https://www.int-res.com/abstracts/esr/v53/p271-293/ |
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