A Bayesian two-stage biomass model applied to the English Channel stock of cuttlefish (Sepia officinalis)

Among the English Channel fishery, the importance of cuttlefish stock has increased, following the cephalopods global landings and market trend. An adapted stock assessment model is needed in order to give good management advice with accurate uncertainties. Age based methods in this species are hampered by time consuming age determination with statoliths. A model requiring few data and adapted to the species life-history is therefore proposed. An assessment of this stock was conducted in June 2015 by fitting a two-stage biomass model on a 22 years’ time-series (1992-2014). The use of a Bayesian framework is particularly adapted for decision making, allowing the propagation of uncertainty in the model. It also permits the use of different sources of information, and is particularly well suited to face the lack of data. In this exercise we implement the two-stage biomass model into a Bayesian framework. We also improve the Bayesian model by using length frequency data and a mortality model to better estimate the biomass growth parameter g. We compare results of two models: one with fixed g and one with time-varying g. The last one is more realistic and model fit is better.