The English Channel stock of Sepia officinalis: Modelling variability in abundance and impact of the fishery

Abstract

The English Channel cuttlefish fishery provides one of the highest cephalopod yields in the north-east Atlantic, with catches by many interacting fishing gears. The aim of this study is to show that population modelling can be developed for this stock and that model estimates are useful to address practical issues such as diagnostics of fishing pressure and interactions between fishing fleets. Abundance indices were calculated from catch and effort data using a General Linear Model and stock size was assessed with analytical methods based on biological data collected since 1996. Cohort analysis was carried out on six cuttlefish cohorts on a monthly basis using catch-at-age data and the results were integrated in a Thomson and Bell model to simulate production and biomass of the stock. GLM indices indicated a strong seasonal pattern of abundance with a peak of adult abundance in spring and of juveniles in autumn. Cohort analysis estimates show that recruitment can vary by a factor of 2 between years. Estimated fishing mortalities indicate rather stable exploitation patterns. Diagnostics show that each studied cohort was generally fully exploited, and do not reveal evidence of recruitment over-fishing. Simulations of fishing fleet interactions for the resource show that otter trawls play a dominant role in cohort exploitation whereas artisanal trap net fisheries are described as “dependent” on other gears. In spite of biological differences between teleost fishes and cephalopods, classical age-based models can be adapted to cuttlefish to provide fishery management advice. Although marked over-fishing was not observed, the studied cohorts were subjected to high catch rates and there is little scope for increased exploitation. Interactions between the fishing fleets involved underline the point that management in the coastal zone alone is not sufficient to ensure sustainable exploitation in a migrating species.