Machine Learning Applications for Fisheries:At Scales from Genomics to Ecosystems

Kühn, Bernhard, Cayetano, Arjay, Fincham, Jennifer I., Moustahfid, Hassan, Sokolova, Maria, Trifonova, Neda, Watson, Jordan T., Fernandes-Salvador, Jose A. and Uusitalo, Laura (2025) Machine Learning Applications for Fisheries:At Scales from Genomics to Ecosystems. Reviews in Fisheries Science and Aquaculture, 33 (2). pp. 334-357. ISSN 2330-8249

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Abstract

Fisheries science aims to understand and manage marine natural resources. It relies on resource-intensive sampling and data analysis. Within this context, the emergence of machine learning (ML) systems holds significant promise for understanding disparate components of these marine ecosystems and gaining a greater understanding of their dynamics. The goal of this paper is to present a review of ML applications in fisheries science. It highlights both their advantages over conventional approaches and their drawbacks, particularly in terms of operationality and possible robustness issues. This review is organized from small to large scales. It begins with genomics and subsequently expands to individuals (catch items), aggregations of different species in situ, on-board processing, stock/populations assessment and dynamics, spatial mapping, fishing-related organizational units, and finally ecosystem dynamics. Each field has its own set of challenges, such as pre-processing steps, the quantity and quality of training data, the necessity of appropriate model validation, and knowing where ML algorithms are more limited, and we discuss some of these discipline-specific challenges. The scope of discussion of applied methods ranges from conventional statistical methods to data-specific approaches that use a higher level of semantics. The paper concludes with the potential implications of ML applications on management decisions and a summary of the benefits and challenges of using these techniques in fisheries.

Item Type:	Article
Additional Information:	We would like to thank all members of ICES WGMLEARN working group for the discussions that helped in shaping this review. Particular thanks to the chairs Ketil Malde and Jean-Olivier Irisson for their invitation to the topic and general support and comments of Sven Kupschus on the initial ideas of this review. Neither the European Union nor the granting authority can be held responsible for them. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Fundación Biodiversidad.
Uncontrolled Keywords:	management,marine science,monitoring,ecology, evolution, behavior and systematics,aquatic science,management, monitoring, policy and law,sdg 14 - life below water ,/dk/atira/pure/subjectarea/asjc/1100/1105
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	23 Feb 2026 16:30
Last Modified:	01 Mar 2026 07:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/102028
DOI:	10.1080/23308249.2024.2423189

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