[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117856-en":3,"doc-seo-117856-105":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},117856,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine learning in marine ecology - an overview of techniques and applications","Machine learning approaches are surveyed for marine ecology, focusing on practical techniques and the scope of applications across ocean data and ecosystems. The review addresses how different modeling and inference strategies can be used to analyze complex, high-dimensional observations, including environmental variables and biological signals. It synthesizes methodological considerations, highlighting strengths, limitations, and requirements for reliable performance in marine research contexts. The article also outlines application areas where these methods support understanding, prediction, and decision-making in ecological studies.","This is an electronic reprint of the original article.  \nThis reprint may differ from the original in pagination and typographic detail.  \nAuthor(s): Peter Rubbens, Stephanie Brodie, Tristan Cordier et al.  \nTitle: Machine learning in marine ecology: an overview of techniques and applications  \nYear: 2023  \nVersion: Publisher’s version  \nCopyright: The author(s) 2023  \nRights: CC BY 4.0  \nRights url: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \n[Please cite the original version:](Please cite the original version:)  \nPeter Rubbens, Stephanie Brodie, Tristan Cordier et al. (2023) Machine learning in marine ecology: an overview of techniques and applications. ICES Journal of Marine Science. doi:10 . 1093/icesjms/fsad100  \nICES Journal of Marine Science, 2023, 0, 1–25 DOI: 10. 1093/icesjms/fsad100  \nReview Article  \nMachine learning in marine ecology: an overview of techniques and applications  \nPeter Rubbens 1,2 , Stephanie Brodie3 , Tristan Cordier4,5 , Diogo Destro Barcellos6 , Paul Devos7 , Jose A. Fernandes-Salvador8 , Jennifer I Fincham 9 , Alessandra Gomes6 , Nils  \nOlav Handegard 10 , Kerry Howell 11 , Cédric Jamet 12 , Kyrre Heldal Kartveit10 ,  \nHassan Moustahfid 13 , Clea Parcerisas 1,7 , Dimitris Politikos 14 , Raphaëlle Sauzède 15 , Maria Sokolova16 , Laura Uusitalo 17,18 , Laure Van den Bulcke 19,20 , Aloysius T. M. van Helmond21 , Jordan T. Watson 22,23 , Heather Welch3 , Oscar Beltran-Perez 24 ,  \nSamuel Chaffron25,26 , David S. Greenberg27 , Bernhard Kühn28 , Rainer Kiko 29,30 , Madiop Lo31 , Rubens M. Lopes 6 , Klas Ove Möller 32 , William Michaels33 , Ahmet Pala34,10 ,  \nJean-Baptiste Romagnan35 , Pia Schuchert36 , Vahid Seydi37 , Sebastian Villasante38 , Ketil Malde 10,39 , and Jean-Olivier Irisson 29 ,*  \n1 Flanders Marine Institute (VLIZ), 8400 Oostende, Belgium  \n2 Kytos BV, Technologiepark-Zwijnaarde 82, 9052 Gent, Belgium  \n3 Institute of Marine Science , University of California Santa Cruz, Santa Cruz, CA 95064, USA  \n4 Department of Genetics and Evolution, University of Geneva, 1205 Geneva, Switzerland  \n5 NORCE Climate, NORCE Norwegian Research Centre AS, Bjerknes Centre for Climate Research, Jahnebakken 5, 5007 Bergen, Norway 6 Oceanographic Institute, University of São Paulo, Praça do Oceanográfico, 191, 05508-120, São Paulo, Brazil  \n7 Department of Information Technology, Research group WAVES, Ghent University, Tech Lane Ghent Science Park, 126, B-9058 Gent, Belgium  \n8AZTI, Marine Research, Basque Research and Technology Alliance (BRTA) . Txatxarramendi Ugartea z/g, 48395 Sukarrieta, Spain  \n9 Cefas, Pakefield Road, Lowestoft, Suffolk NR33 0HT, UK  \n10 Institute of Marine Research, Nykirkekaien 1, 5005 Bergen, Norway  \n11 School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK  \n12 Université du Littoral Côte d’Opale, CNRS, Univ. Lille, IRD, UMR 8187, LOG, Laboratoire d’Océanologie et de Géosciences, F-62930 Wimereux, France  \n13 National Oceanic and Atmospheric Administration, US Integrated Ocean Observing System, Silver Spring, MD 20910, USA  \n14 Institute of Marine Biological Resources and Inland, Hellenic Centre for Marine Research, 16452 Argyroupoli, Greece  \n15 Sorbonne Université, CNRS, Institut de la Mer de Villefranche, FR3761, F-06230 Villefranche-Sur-Mer, France  \n16Wageningen University and Research, Droevendaalsesteeg 1, Building 107, 6708 PB Wageningen, The Netherlands  \n17 Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland  \n18 Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland  \n19 Flanders Research Institute for Agriculture, Fisheries and Food, Marine Research, Jacobsenstraat 1, 8400 Ostend, Belgium  \n20 Department of Data Analysis and Mathematical Modelling—Knowledge-based Systems Research Group, University of Ghent, Coupure Links 653, 9000 Gent, Belgium  \n21Wageningen University and Research, Wageningen Marine Researc","cbCaic3aoj4xRA7T","https://ap.wps.com/l/cbCaic3aoj4xRA7T","pdf",1853438,1,26,"English","en",105,"# Overview\n## Techniques and methodological considerations\n## Applications in marine ecology\n## Strengths, limitations, and requirements for reliable use","[{\"question\":\"What is the main focus of this review on machine learning in marine ecology?\",\"answer\":\"It provides an overview of techniques and applications of machine learning for marine ecology research, emphasizing how methods can be applied to marine data and ecosystem questions.\"},{\"question\":\"Which kinds of marine research inputs are discussed for using machine learning?\",\"answer\":\"The review centers on complex ocean observations, including environmental variables and biological signals, and how they can be analyzed using different modeling strategies.\"},{\"question\":\"How does the review treat the reliability of machine learning results in marine contexts?\",\"answer\":\"It highlights methodological requirements and notes strengths and limitations, aiming to support dependable performance when translating models to ecological understanding and prediction.\"}]","Machine learning in marine ecology - 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