Incorporating Stakeholder Knowledge Into a Complex Stock Assessment Model, the Case of Eel Recruitment

Abstract

Mistrust between scientists and non-scientist stakeholders is a key challenge in fishery management. This problem is exacerbated with the use of complex models to support management: these models suffer from difficulties in communicating their results and a lack of confidence from end users. The European eel is an illustrative example; its complex life cycle raises problems of coordination and discussion among the multiple actors involved in the management of the species. The GEREM model has been proposed as a tool for estimating recruitment, but its complexity, which is essential for addressing the characteristics of the species, makes it difficult to understand and accept by all stakeholders. In the context of the SUDOANG project, we proposed a co-parametrization of this assessment model to tackle this mistrust. Through the use of various questionnaires and appropriate statistical analyses, stakeholders were involved in two important choices for the model (zone definition and prior construction). Regular workshops and presentations were organised to explain the model rationales and to gather feedback and expectations. The results show that stakeholders have very similar perceptions of the potential definitions of sub-areas of recruitment in south-western Europe, and these perceptions are consistent with the underlying environmental conditions. On the other hand, the stakeholders have contrasting opinions about the exploitation rates of fisheries in different river basins, and the use of their knowledge currently has little effect on GEREM estimates. More importantly, the overall approach of this study is thought to have reconstructed the trust and confidence among participants.

Thirteen novel ideas and underutilised resources to support progress towards a range‐wide American eel stock assessment

“…small dams), could aid the conservation of a wide range of aquatic fauna, including eels (LaDeau et al, 2017). In Europe, collaboration amongst diverse environmental stakeholders and widespread use of crowdsourcing have put needed data in the hands of eel biologists (Belletti et al, 2020; Drouineau et al, 2021). The recent development of a machine learning algorithm that uses geospatial and lidar data to identify dams, even small ones, with reasonable accuracy (true positive rate 89%, false positive rate 1.2%) (Buchanan et al, 2022) may open the door to comprehensive inventories of dams of all sizes.…”