Evaluating the role of bycatch reduction device design and fish behavior on Pacific salmon (Oncorhynchus spp.) escapement rates from a pelagic trawl

Yochum, Noëlle; Stone, Michael K.; Breddermann, K.; Berejikian, Barry A.; Gauvin, John R.; Irvine, David J.

doi:10.1016/j.fishres.2020.105830

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…Such Images systematic behavioral responses could thus be used to train an AI model which could then be used to automatically analyze replicates of additional trials. Similarly, AI applications would enable to amplify the number of replicates of sea or laboratory trials, for example when assessing how changes in the positions of stimuli influences species behaviors (Larsen et al, 2017;Yochum et al, 2021).…”

Section: Towards Smart Fishingmentioning

confidence: 99%

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

2023

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Through the advancement of observation systems, our vision has far extended its reach into the world of fishes, and how they interact with fishing gears—breaking through physical boundaries and visually adapting to challenging conditions in marine environments. As marine sciences step into the era of artificial intelligence (AI), deep learning models now provide tools for researchers to process a large amount of imagery data (i.e., image sequence, video) on fish behavior in a more time-efficient and cost-effective manner. The latest AI models to detect fish and categorize species are now reaching human-like accuracy. Nevertheless, robust tools to track fish movements in situ are under development and primarily focused on tropical species. Data to accurately interpret fish interactions with fishing gears is still lacking, especially for temperate fishes. At the same time, this is an essential step for selectivity studies to advance and integrate AI methods in assessing the effectiveness of modified gears. We here conduct a bibliometric analysis to review the recent advances and applications of AI in automated tools for fish tracking, classification, and behavior recognition, highlighting how they may ultimately help improve gear selectivity. We further show how transforming external stimuli that influence fish behavior, such as sensory cues and gears as background, into interpretable features that models learn to distinguish remains challenging. By presenting the recent advances in AI on fish behavior applied to fishing gear improvements (e.g., Long Short-Term Memory (LSTM), Generative Adversarial Network (GAN), coupled networks), we discuss the advances, potential and limits of AI to help meet the demands of fishing policies and sustainable goals, as scientists and developers continue to collaborate in building the database needed to train deep learning models.

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Section: Towards Smart Fishingmentioning

confidence: 99%

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

2023

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“…There are also many instances of effective collaboration towards the development and improvement of gear (Walsh et al, 2002;Harley and Robinson, 2008;Feekings et al, 2019;Merrifield et al, 2019), assessment of the selectivity of gear (Graham et al, 2007;Baker et al, 2011;Baker et al, 2014;O'Neill et al, 2019), analysis of selectivity in catch and surveys (Rose et al, 2010;Somerton et al, 2011;Veiga-Malta et al, 2019), analyses of management approaches (Smith et al, 2007;Smeltz et al, 2019) and conservation engineering solutions to mitigate fisheries impacts on marine environment (Kaiser et al, 2016;Österblom et al, 2020) or benthic substrates and communities (Rose et al, 2000;Rooper et al, 2011). Additionally, there are many examples of effective collaborative approaches to minimize or reduce bycatch and incidental mortality in protected or non-target species (Gauvin and Rose, 2001;Carruthers and Neis, 2011;Arkhipkin et al, 2021;Kroska et al, 2021;Yochum et al, 2021). There are also examples of effective scienceindustry collaboration to inform harvest control rules and management planning (Davis, 2008;Heller-Shipley et al, 2021).…”

Section: Secondary Considerations and Inferencesmentioning

confidence: 99%

A new era for science-industry research collaboration – a view towards the future

Baker,

Steins,

Pastoors

et al. 2023

Front. Mar. Sci.

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Direct engagement of the fishing industry in the provision and co-creation of knowledge and data for research and management is increasingly prevalent. In both the North Atlantic and North Pacific, enhanced and targeted engagement is evident. More is needed. Science-Industry collaborative approaches to developing questions, collecting data, interpreting data, and sharing knowledge create opportunities for information transfer and improved understanding of ecosystem interactions, stock dynamics, economic incentives, and response to management. These collaborations require clear communication and awareness of objectives and outcomes. These initiatives also require careful attention to conditions and interactions that foster respect, trust, and communication. Respect is critical and entails acknowledging the respective skills and expertise of both scientists and fishers. Trust is needed to build confidence in the information developed and its use. Communication is essential to maintain relationships and leverage shared insights. To assess current trends and future opportunities related to this type of engagement, we convened a networking session of research scientists, industry scientists, industry leaders, and fishers at the Annual Science Meeting of the International Council for the Exploration of the Sea (ICES) to address the following questions: (1) What are scientific needs that could be addressed with industry-collected data or knowledge? And (2) How can science-industry collaboration be made sustainable? Here we identify opportunities and acknowledge challenges, outline necessary conditions for respectful and sustainable collaborative research, and highlight ways to promote stakeholder involvement in developing science. We address industry concerns and solicit industry advice. We also address challenges to scientists in ensuring standards for scientific data, conflict of interest, and applying information to advise management. The discussions in this session and subsequent correspondence have led to a set of guidelines and best practices that provide a framework to advance further collaboration between industry and research science. We identify opportunities for directed engagement. We also detail potential approaches to clarify expectations and develop avenues for iterative communication and engagement to sustain collaborative efforts over time. The intent is to improve and expand data streams and contextual understanding of ecosystem processes, stock assessment, and socio-economic dynamics to the benefits of science and industry alike.

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“…There are also many instances of effective collaboration towards the development and improvement of gear (Walsh et al, 2002;Harley and Robinson, 2008;Merrifield et al, 2019), assessment of the selectivity of gear (Graham et al, 2007;Baker et al, 2011;, analysis of selectivity in catch and surveys (Rose et al, 2010;Somerton et al, 2011;Veiga-Malta et al, 2019), analyses of management approaches (Smith et al, 2007;Smeltz et al, 2019) and conservation engineering solutions to mitigate fisheries impacts on marine environment (Kaiser et al, 2016;Österblom et al, 2020) or benthic substrates and communities (Rose et al, 2000;Rooper et al, 2011). Additionally, there are many examples of effective collaborative approaches to minimize or reduce bycatch and incidental mortality in protected or non-target species (Gauvin and Rose, 2001;Kroska et al, 2021;Yochum et al, 2021). There are also examples of effective scienceindustry collaboration to inform harvest control rules and management planning (Davis, 2008;Heller-Shipley et al, 2021).…”

Section: Secondary Considerations and Inferencesmentioning

confidence: 99%

Co-creating Knowledge with Fishers: Challenges and Lessons for Integrating Fishers' Knowledge Contributions into Marine Science in Well-Developed Scientific Advisory Systems

2023

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Evaluating the role of bycatch reduction device design and fish behavior on Pacific salmon (Oncorhynchus spp.) escapement rates from a pelagic trawl

Cited by 10 publications

References 21 publications

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

A new era for science-industry research collaboration – a view towards the future

Co-creating Knowledge with Fishers: Challenges and Lessons for Integrating Fishers' Knowledge Contributions into Marine Science in Well-Developed Scientific Advisory Systems

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