Evaluating deepwater fisheries management strategies using a mixed-fisheries and spatially explicit modelling framework

Wehave used in this study a spatially explicit bioeconomic modelling framework to evaluate management strategies, building in both datarich and data-limited harvest control rules (HCRs), for a mix of deepwater fleets and species, on which information is variable. The main focus was on blue ling (Molva dypterygia). For that species, both data-rich and data-limited HCRs were tested, while catch per unit effort (CPUE) was used either to tune stock assessments, or to directly trigger management action. There were only limited differences between the performances of both HCRs when blue ling biomass was initialized at the current level, but blue ling recovered more quickly with the data-rich HCR when its initial biomass was severely depleted. Both types of HCR lead, on average, to a long-term recovery of both blue ling and saithe (Pollachius virens) stocks, and some increase in overall profit. However, that improvement is not sufficient to guarantee sustainable exploitation with a high probability. Blue ling CPUE did not always adequately reflect trends in biomass, which mainly resulted from fleet dynamics, possibly in combination with density-dependence. The stock dynamics of roundnose grenadier (Coryphaenoides rupestris), black scabbardfish (Aphanopus carbo) and deepwater sharks (Centrophorus squamosus and Centroscymnus coelolepis) were little affected by the type of HCR chosen to manage blue ling.