Oral Presentation Australian Society for Fish Biology Conference 2025

Reeling in innovation: Identifying barriers and enablers influencing the successful adoption of AI for fisheries management (123843)

Jessica Hunter 1 , Mark Freeman 1 , Rachel Nichols 1
  1. University of Wollongong, Ainslie, ACT, Australia

Technological creep has increased the efficiency of industrial-scale fishing, with a global marine fleet of over 3.7 million vessels underpinning food security, livelihoods, employment and international trade (Rousseau et al., 2019). Composed of key components data collection, stock assessment, regulation setting and compliance, fisheries management is the science and practice of regulating this activity (Cochrane, 2002; King, 2007). As artificial intelligence (AI) offers to enhance each of these components the challenge is to ensure innovation aligns with long-term ecological sustainability of fish stocks rather than accelerate overexploitation. Successful adoption of AI is therefore best characterised by its deployment within an adaptive and self-correcting management system. The aim of this research is to identify barriers and enablers influencing the successful adoption of AI in fisheries by reviewing existing literature.

Using the Arksey and O'Malley framework a scoping review will be conducted by defining research question/s and performing a systematic search strategy across multiple databases. Results will be reported by following PRISMA-ScR guidelines including definition of search strategy, databases and screening process, a summary of key findings, categorised barriers/enablers and trends. This will be followed by discussion of implications, challenges, knowledge gaps and future research directions. 

Many new and promising avenues of innovation with AI are underway including species identification (Eickholt et al., 2025), stock assessment (Piatinskii et al., 2024; Rodríguez et al., 2024) and detection of illegal, unreported and unregulated (IUU) fishing (Brown et al., 2024; Welch et al., 2024). By considering these innovations within a system designed to amplify their benefits and mitigate their risks, a clearer pathway for ongoing adoption can emerge.

There will be an emphasis on ways to develop AI responsibly with discussion of existing bias in the fisheries sector. While collaboration and trust between fisheries stakeholders, including small and large scale fisheries, is important for management, using advanced technologies it will become imperative (Wing & Woodward, 2024). It is expected that a key enabler of AI adoption will be novel, thoughtful collaboration between diverse fisheries stakeholders aligning well with the theme of the conference.

  1. Brown, S., Katz, D., Korotovskikh, D., & Kullman, S. (2024). Detecting illegal, unreported, and unregulated fishing through AIS data and machine learning approaches. In 2024 Systems and Information Engineering Design Symposium (SIEDS) (pp. 319-324). IEEE.
  2. Cochrane, K. L. (2002). Fisheries management. In FAO Fisheries Technical Guidelines for Responsible Fisheries No. 4. Food and Agriculture Organization of the United Nations. Retrieved from https://www.fao.org/4/y3427e/y3427e03.htm
  3. Eickholt, J., Gregory, J., & Vemuri, K. (2025). Advancing Fisheries Research and Management with Computer Vision: A Survey of Recent Developments and Pending Challenges. Fishes, 10(2), 74.
  4. King, M. (2007). Fisheries biology, assessment and management (2nd ed., p. 274). Wiley-Blackwell.
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  6. Rodríguez, M. A., Lemaire, M., Fugère, V., Barrette, M. F., Gagné, S., Leclerc, V., & Beisner, B. E. (2024). Assessing the potential responses of 10 important fisheries species to a changing climate with machine learning and observational data across the province of Québec. Canadian Journal of Fisheries and Aquatic Sciences.
  7. Rousseau, Y., Watson, R. A., Blanchard, J. L., & Fulton, E. A. (2019). Evolution of global marine fishing fleets and the response of fished resources. Proceedings of the National Academy of Sciences, 116(25), 12238-12243.
  8. Welch, H., Ames, R. T., Kolla, N., Kroodsma, D. A., Marsaglia, L., Russo, T., & Hazen, E. L. (2024). Harnessing AI to map global fishing vessel activity. One Earth, 7(10), 1685-1691.
  9. Wing, K., & Woodward, B. (2024). Advancing artificial intelligence in fisheries requires novel cross-sector collaborations. ICES Journal of Marine Science, 81(10), 1912-1919.