A Bayesian two-stage biomass model for stock assessment of data-limited species: An application to cuttlefish (Sepia officinalis) in the English Channel

Abstract : Cuttlefish is a key commercial species in the English Channel fishery in terms of landings and value. Age based assessment methods are limited by time-consuming age determination with statoliths and the lack of stock assessment models tailored to this data-limited species. A two-stage biomass model is developed in the Bayesian state-space modelling framework that allows inferences to be made on the stock biomass at the start, middle and end of each fishing seasons between 1992 and 2014, while accounting for both process and measurement errors and to assimilate various sources of information. A method that uses ancillary length-frequency data is developed to provide an informative prior distribution for the biomass growth rate parameter g (E=0.89) and its annual variability (CV=0.1). The new model is a substantial improvement on the existing stock assessment method used by the International Council for the Exploration of the Seas. Taking into consideration a time-varying g parameter provides a more ecologically meaningful model with regard to the sensitivity of the cuttlefish population dynamics to environmental fluctuations and improves model fit. The model also provides predictions of the unexploited biomass in winter, which is based on survey data, and helps manage the stock in the event of strong depletion. (C) 2017 Elsevier B.V. All rights reserved.
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Fisheries Research, Elsevier, 2017, 191, pp.131-143. 〈10.1016/j.fishres.2017.03.010〉
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Soumis le : mercredi 12 juillet 2017 - 11:36:38
Dernière modification le : mardi 29 mai 2018 - 16:20:02

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J. Alemany, Etienne Rivot, E. Foucher, J. Vigneau, J. P. Robin. A Bayesian two-stage biomass model for stock assessment of data-limited species: An application to cuttlefish (Sepia officinalis) in the English Channel. Fisheries Research, Elsevier, 2017, 191, pp.131-143. 〈10.1016/j.fishres.2017.03.010〉. 〈hal-01561014〉

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