FN Archimer Export Format
PT C
TI Stock assessment models for short-lived species in data-limited situations. Case study of the English Channel stock of cuttlefish.
OT Modèles d'évaluation de stock pour les espèces à vie courte dans des situations de données limitées. Cas d'étude du stock de seiche de Manche.
BT 
AF ALEMANY, Juliette
   FOUCHER, Eric
   RIVOT, Etienne
   VIGNEAU, Joel
   ROBIN, Jean-Paul
AS 1:1;2:1;3:2;4:1;5:3;
FF 1:PDG-RBE-HMMN-RHPEB;2:PDG-RBE-HMMN-RHPEB;3:;4:PDG-RBE-HMMN-RHPEB;5:;
C2 IFREMER, FRANCE
   AGROCAMPUS OUEST, FRANCE
   UNIV CAEN, FRANCE
SI PORT-EN-BESSIN
SE PDG-RBE-HMMN-RHPEB
UR https://archimer.ifremer.fr/doc/00377/48773/49171.pdf
LA English
DT Poster
AB Among the English Channel fishery, the importance of cuttlefish stock has increased, following the cephalopods global landings and market trend. The stock is currently managed at regional scale but not by European regulations, although it is a shared species targeted by French and British fishing fleets at several stages of its life-cycle and across much of its distributional range. An assessment of this stock was conducted in June 2014 by fitting a two-stage biomass model on a 22 years’ time-series (1992-2013). As the assumptions of the model are based on a simplified life-cycle, it would be appropriate to compare the results with outputs from other models in order to obtain reliable biomass estimations. The final aim is to produce reliable management rules to assure a sustainable harvest rate. The use of a Bayesian framework is particularly adapted for decision making, allowing the propagation of uncertainty in the model and the use of prior knowledge on some parameter distributions. Therefore, we implemented the two-stage biomass model into a Bayesian framework and compared the results with the outputs of the initial fit. We also applied a multi-annual generalized depletion model to the English Channel cuttlefish stock. We found similar trends of biomass estimates for both models. The Bayesian model outputs showed a smaller range of variation than the initial fit. These results allow a first comparison of the initial model outputs. But the Bayesian model could be improved and particular attention should be paid to the growth parameter g because of the high sensitivity of model outputs to its value.
PY 2015
PD MAY
ID 48773
ER

EF