FN Archimer Export Format
PT J
TI A Bayesian two-stage biomass model for stock assessment of data-limited species: An application to cuttlefish ( Sepia officinalis ) in the English Channel
BT 
AF ALEMANY, Juliette
   RIVOT, Etienne
   FOUCHER, Eric
   VIGNEAU, Joel
   ROBIN, Jean-Paul
AS 1:1,3;2:2;3:1;4:1;5:3;
FF 1:PDG-RBE-HMMN-RHPEB;2:;3:PDG-RBE-HMMN-RHPEB;4:PDG-RBE-HMMN-RHPEB;5:;
C1 IFREMER, Port En Bessin, France.
   INRA, UMR ESE Ecol & Ecosyst Hlth 985, Agrocampus Ouest, F-35042 Rennes, France.
   Univ Caen, Res Unit BOREA Biol Aquat Organisms & Ecosyst, Normandy, France.
C2 IFREMER, FRANCE
   INRA, FRANCE
   UNIV CAEN, FRANCE
SI PORT-EN-BESSIN
SE PDG-RBE-HMMN-RHPEB
IN WOS Ifremer jusqu'en 2018
   copubli-france
   copubli-p187
   copubli-univ-france
IF 1.874
TC 6
UR https://archimer.ifremer.fr/doc/00376/48766/49289.pdf
LA English
DT Article
DE ;English Channel;Cuttlefish;Sepia officinalis;Bayesian state-space model;Data-limited stock;Two-stage biomass model
AB 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.
PY 2017
PD JUN
SO Fisheries Research
SN 0165-7836
PU Elsevier Science Bv
VL 191
UT 000402357600016
BP 131
EP 143
DI 10.1016/j.fishres.2017.03.010
ID 48766
ER

EF