[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124407-en":3,"doc-seo-124407-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124407,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Predicting Pacific saury fishing sites using machine learning and spatial environmental variables reflecting recent eastward shifts","Recent declines in Pacific saury catches in the Northwest Pacific and an eastward shift of fishing grounds have intensified the need for more effective resource management. To identify environmental drivers shaping Pacific saury fishing grounds, this study builds machine-learning prediction models using spatial environmental variables. Models integrate fishing site and pseudoabsence data with high-resolution oceanographic fields from FRA-ROMS. Three variable representations are tested, and preserving spatial structure via a 2D grid layout improves performance. Results mirror the recent eastward shift, with temperature from ocean circulation emerging as a key factor; a CNN achieves the best replication, with recall 45.0% and precision 95.4%.","TYPE Original Research PUBLISHED 17 September 2025 DOI 10.3389/fmars.2025.1584413  \nOPEN ACCESS  \nEDITED BY  \nTomaso Fortibuoni,  \nIstituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA), Italy  \nREVIEWED BY  \nNerea Goikoetxea,  \nTechnology Center Expert in Marine and Food Innovation (AZTI), Spain  \nJiajun Li,  \nChinese Academy of Fishery Sciences (CAFS), China  \nVilma Viviana Ojeda Caicedo,  \nUniversidad Tecnologica de Bolivar, Colombia Nima Farchadi,  \nSan Diego State University, United States  \n*CORRESPONDENCE  \nTaiga Asakura  \n [asakura_taiga21@fra.go.jp](asakura_taiga21@fra.go.jp)  \nRECEIVED 27 February 2025  \nACCEPTED 26 August 2025  \nPUBLISHED 17 September 2025  \nCITATION  \nAsakura T, Mekuchi M, Fuji T and Suyama S  \n(2025) Predicting Pacific saury fishing sites using machine learning and spatial environmental variables reflecting recent eastward shifts.  \nFront. Mar. Sci. 12:1584413 .  \ndoi: 10.3389/fmars.2025.1584413  \nCOPYRIGHT  \n© 2025 Asakura, Mekuchi, Fuji and Suyama. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPredicting Pacific saury fishing sites using machine learning and spatial environmental variables reflecting recent eastward shifts  \nTaiga Asakura 1*, Miyuki Mekuchi 1, Taiki Fuji 2 and Satoshi Suyama 3  \n1 Bioinformatics and Biosciences Division, Fisheries Stock Assessment Center, Fisheries Resources Institute, Japan Fisheries Research and Education Agency, Yokohama, Japan, 2 Highly Migratory Resources Division, Fisheries Stock Assessment Center, Fisheries Resources Institute, Japan Fisheries Research and Education Agency, Yokohama, Japan, 3 Highly Migratory Resources Division, Fisheries Stock Assessment Center, Fisheries Resources Institute, Japan Fisheries Research and Education Agency, Hachinohe, Japan  \nIn recent years, the Northwest Pacific has seen a decline in Pacific saury (Cololabis saira) catch and an eastward shift of fishing grounds, both of which have posed increasing challenges for effective resource management. To identify environmental drivers underlying the formation of Pacific saury fishing grounds, we developed machine learning-based prediction models using spatial environmental variables . Our models combined fishing site and pseudoabsence data with high-resolution oceanographic data from the Japan Fisheries Research and Education Agency Regional Ocean Modeling System (FRA-ROMS) . We employed three machine learning methods to evaluate three types of explanatory variable representations: averaged, vectorized, and spatially structured. The results demonstrated that preserving spatial structure using a two-dimensional grid layout improved model performance. Our prediction results reflected the recent eastward shifting fishing grounds, suggesting a strong influence of environmental factors, particularly water temperature derived from the ocean circulation model. The convolutional neural network model, which best replicated the eastward shift of fishing sites, achieved a recall of 45.0% and a precision of 95.4%, although its performance declined under higher environmental novelty, which was associated with low-catch years (2020-2022) . By evaluating how different spatial representations of environmental variables affect model performance, this study demonstrates that incorporating spatial structure improves predictive ability and enables models to capture recent eastward shifts in fishing activity under changing ocean conditions.  \nKEYWORDS  \nPacific saury, fishing sites prediction, machine learning, environmental variables, random forest, convolutional neural network (CNN)  \nFrontiers in Marine ","cbCaijp0sIR8wXHv","https://ap.wps.com/l/cbCaijp0sIR8wXHv","pdf",5955160,1,12,"English","en",105,"# Introduction\n# Methods\n## Variable representations\n## Machine learning models\n# Results\n## Model performance and spatial structure\n## Eastward shift prediction","[{\"question\":\"What problem does this study address about Pacific saury fisheries?\",\"answer\":\"It addresses declining catches and an eastward shift in fishing grounds in the Northwest Pacific, which complicates effective resource management.\"},{\"question\":\"What data and modeling approach are used to predict fishing sites?\",\"answer\":\"The models use fishing site and pseudoabsence data combined with high-resolution oceanographic variables from FRA-ROMS, evaluated with three machine-learning methods.\"},{\"question\":\"Why does preserving spatial structure improve prediction performance?\",\"answer\":\"Using a two-dimensional grid layout to retain spatial structure yields better model performance and helps the predictions reflect recent eastward shifting fishing activity.\"}]","Predicting Pacific saury fishing sites using machine learning and spatial environmental variables reflecting recent eastward shifts | 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problem does this study address about Pacific saury fisheries?","Question",{"text":75,"@type":76},"It addresses declining catches and an eastward shift in fishing grounds in the Northwest Pacific, which complicates effective resource management.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and modeling approach are used to predict fishing sites?",{"text":80,"@type":76},"The models use fishing site and pseudoabsence data combined with high-resolution oceanographic variables from FRA-ROMS, evaluated with three machine-learning methods.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does preserving spatial structure improve prediction performance?",{"text":84,"@type":76},"Using a two-dimensional grid layout to retain spatial structure yields better model performance and helps the predictions reflect recent eastward shifting fishing 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