[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128000-en":3,"doc-seo-128000-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128000,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine-learning aiding sustainable Indian Ocean tuna purse seine fishery - Published version","Machine-learning models support a more sustainable Indian Ocean tuna purse seine fishery by targeting efficient fishing and reduced bycatch. The study addresses increasing fuel use and emissions alongside the need to minimise catches of vulnerable species. Using historical catch records and environmental variables, it models target tropical tunas and silky shark bycatch, enabling forecasting of fishing grounds and improving decision-making. Results report accuracy values of 0.718 and 0.728 for SKJ and YFT, and 0.842 for silky shark presence/absence.","This is an electronic reprint of the original article.  \nThis reprint may differ from the original in pagination and typographic detail.  \nAuthor(s): Nerea Goikoetxea, Izaro Goienetxea, Jose A. Fernandes-Salvador, Nicolas Goñi, Igor Granado, Iñaki Quincoces, Leire Ibaibarriaga, Jon Ruiz, Hilario Murua, Ainhoa Caballero  \nTitle: Machine-learning aiding sustainable Indian Ocean tuna purse seine fishery  \nYear: 2024  \nVersion: Published version  \nCopyright: The Author(s) 2024  \nRights: CC BY-NC-ND 4.0  \nRights url:  [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \n[Please cite the original version:](Please cite the original version:)  \nNerea Goikoetxea, Izaro Goienetxea, Jose A. Fernandes-Salvador, Nicolas Goñi, Igor Granado, Iñaki Quincoces, Leire Ibaibarriaga, Jon Ruiz, Hilario Murua, Ainhoa Caballero, Machine-learning aiding sustainable Indian Ocean tuna purse seine fishery, Ecological Informatics, Volume 81, 2024, 102577, ISSN 1574-9541, [https://doi.org/10.1016/j.ecoinf.2024.102577](https://doi.org/10.1016/j.ecoinf.2024.102577) .  \nEcological Informatics 81 (2024) 102577  \nContents lists available at ScienceDirect  \nEcological Informatics  \njournal [homepage:](homepage: www.elsevier.com/locate/ecolinf)[ www.elsevier.com/locate/ecolinf](homepage: www.elsevier.com/locate/ecolinf)  \n| Machine-learning aiding sustainable Indian Ocean tuna purse seine fishery  \u003Cbr>Nerea Goikoetxea a, *, Izaro Goienetxeab, Jose A. Fernandes-Salvador a, Nicolas Go˜ni c, Igor Granado b, I˜naki Quincocesa, Leire Ibaibarriagaa, Jon Ruiz a, Hilario Muruad, Ainhoa Caballerob\u003Cbr>a AZTI, Marine Research, Basque Research and Technology Alliance (BRTA), Txatxarramendi Ugartea z/g, 48395 Sukarrieta, Spain b AZTI, Marine Research, Basque Research and Technology Alliance (BRTA), Herrera kaia, Portualdea z/g, 20110 Pasaia, Spain c LUKE, Natural Resources Institute Finland, It¨ainen Pitk¨akatu 4A, FI-20520 Turku, Finland\u003Cbr>d International Sustainable Seafood Foundation, Pittsburgh, PA 15201, United States |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Sustainable fishing Bycatch\u003Cbr>Machine-learning Tropical tuna\u003Cbr>Fisheries oceanography Species distribution models |  | Among the various challenges facing tropical tuna purse seine fleet are the need to reduce fuel consumption and carbon footprint, as well as minimising bycatch of vulnerable species. Tools designed for forecasting optimum tuna fishing grounds can contribute to adapting to changes in fish distribution due to climate change, by identifying the location of new suitable fishing grounds, and thus reducing the search time. While information about the high probability to find vulnerable species could result in a bycatch reduction. The present study aims at contributing to a more sustainable and cleaner fishing, i.e. catching the same amount of target tuna with less fuel consumption/emissions and lower bycatch. To achieve this, tropical tuna catches as target species, and silky shark accidental catches as bycatch species have been modelled by machine learning models in the Indian Ocean using as inputs historical catch data of these fleets and environmental data. The resulting models show an accuracy of 0.718 and 0.728 for the SKJ and YFT, being the absences (TPR = 0.996 for SKJ and 0.993 for YFT, respectively) better predicted than the high or low catches. In the case of the BET, which is not the main target species of this fleet, the accuracy is lower than that of the previous species. Regarding the silky shark, the presence/absence model provides an accuracy of 0.842. Even though the model's performance has room for improvement, the present work lays the foundations of a process for forecasting fishing grounds avoiding vulnerable species, by only using as input data forecast environmental data provided in near real time by earth observation programs. In the future these models can be improved as more i","cbCaivFk2qj7pMrp","https://ap.wps.com/l/cbCaivFk2qj7pMrp","pdf",2463700,4,1,11,"English","en",105,"# Introduction\n## Sustainability pressures in tropical tuna purse seining\n## Forecasting fishing grounds under climate-driven distribution shifts\n## Modeling approach overview","[{\"question\":\"What sustainability problems does the study focus on for tropical tuna purse seine fleets?\",\"answer\":\"It targets high fuel consumption and greenhouse gas emissions, and the need to minimise bycatch of vulnerable species while maintaining target tuna catches.\"},{\"question\":\"Which species are modeled in the machine-learning framework?\",\"answer\":\"Tropical tuna catches are modeled as target species, and silky shark catches are modeled as the bycatch species.\"},{\"question\":\"What inputs and outputs does the approach use to support fishing-ground decisions?\",\"answer\":\"The models use historical catch data and environmental data to forecast suitable fishing grounds, including estimating presence/absence risk for vulnerable species.\"}]","Machine-learning aiding sustainable Indian Ocean tuna purse seine fishery - 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