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The shifting policy focus towards Ecosystem Based Management in Fisheries (EBFM) requires the integration of knowledge and disciplines and the engagement of stakeholders to support decision-making processes. Scientists contribute to this through (i) participatory research projects, (ii) stakeholder partnerships and (iii) institutional scientific advice processes. Understanding the role of scientists in such processes, the nature of the interactions between scientists, stakeholders and managers in knowledge integration and the link between science and policy is an emerging field of research addressing transdisciplinary challenges. In 2018, Ifremer-UMR AMURE organized the workshop ‘Science, Partnership and Decision-support in Fisheries’ bringing together international scientists from natural and social sciences to conduct a review based on twenty concrete case studies. Findings indicate that science-stakeholder-manager partnerships for decision-support in fisheries can play an essential role in the transition to EBFM. To foster this transition, eight recommendations are presented that cover the roles of the different participants, the expectations of partnerships, capacity building, the integration of the social sciences, and funding structures. Further, it is recommended that future research and innovation framework programmes into sustainable fisheries and other ocean uses should explicitly include mechanisms to foster transdisciplinary approaches and the development of best practices. Building-up networks and developing reflexive approaches to review experiences and practices for transdisciplinary approaches in EFBM decision-support will contribute to design the next generation transdisciplinary platforms and generating actionable knowledge towards EBFM.

Fisheries science and fisheries management advice rely on both scientific and commercial data to estimate the distribution and abundance of marine species. These two data types differ, with scientific data having a broader geographical coverage but less intensity and time coverage compared to commercial data. Here we present a new type of commercial data with high resolution and coverage. To our knowledge, the dataset presented in this study has never been used for scientific purposes. While commercial datasets usually include the total weight by species on per haul basis, the new data also include the commercial size class for the species landed, recorded directly on a haul-by-haul basis. Thus, this dataset has the potential to provide knowledge on landed fish with as high spatio-temporal resolution as when coupling logbooks and sales slips but with the addition of detailed knowledge on the size distribution. Such information may otherwise be obtained through on-board observer programmes but unlike the observers’ data, the dataset presented here is routinely collected on most of the trips of the vessels involved, which means that the coverage of the data for the individual vessel is larger than observers’ data. Furthermore, the risk of changes in fishing behaviour due to the presence of an observer on-board is avoided. This paper describes the coverage and completeness of the dataset, and explores the reliability of the data available. We conclude that the main limitation is the small number of fishing vessels covered by the program, but that the data from those vessels are accurate, detailed, and relatively reliable.

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