Exploring the impact of area exclusions on stock assessment indices using multiobjective optimization

Fisheries surveys that have been conducted for decades are increasingly becoming disrupted as the marine space becomes more crowded. These surveys provide important indices of abundance and biomass for the stock assessment process, and the implications for these indices becomes an increasingly complex problem as multiple areas are excluded. This paper takes Northern Shelf Haddock as a case study and implements an approach using multiobjective optimization (MOO) to explore the cumulative worst-case scenarios for the two indices of abundance used in this stock assessment. Results emphasized the region to the north of Scotland as important for maintaining the indices of abundance for this stock but also indicate that in a cumulative setting other areas may also be key. By demonstrating a first-of-its-kind application of MOO methods to this problem, this study highlights how these can be used to better inform decision-makers to ensure the maintenance of key stock indices.