Aquat. Living Resour.
Volume 26, Number 4, October-December 2013
Deep-Sea Fisheries and Stocks
|Page(s)||355 - 364|
|Section||Deep-Sea Fisheries and Stocks|
|Published online||22 November 2013|
Modelling the dynamics of the deepwater shark Centroscymnus coelolepis off mainland Portugal
Instituto Português do Mar e da Atmosfera (IPMA),
2 CEAUL, Departamento de Matemática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
3 CEAUL, Faculdade de Ciências da Universidade de Lisboa, Portugal
a Corresponding author: email@example.com
Received: 7 February 2013
Accepted: 1 October 2013
A statistical approach to evaluate the temporal trends in the abundance of female Centroscymnus coelolepis in Portuguese waters of ICES division IXa is presented. A state space model is used, which integrates all the available information of the species’ life history as well as knowledge of its biological dynamics. The model involves two processes that run in parallel: a non-observed process (the state process) that describes the annual female population abundance and an observational process of annual fisheries catches in numbers, assumed to be measured with error. Estimation is done within the Bayesian paradigm using sequential importance sampling with resampling. To evaluate the sensitivity of the model to the prior distributions chosen for the parameters, three scenarios with different levels of prior information were considered. Trends in population abundance level and the abundance levels themselves are quite similar in the two scenarios using biological information, but the model that incorporated all the available biological information in the priors provided the best fit to the observed data. The results indicate that taking into account the main biological drivers and the fishing information in the same state space model provides a coherent picture of the population abundance trends, further suggesting that the fishing impact on the population inhabiting Portuguese mainland waters was low.
Key words: Abundance temporal trend / State-space model / Bayesian analysis / Portuguese dogfish / NE Atlantic Ocean
© EDP Sciences, IFREMER, IRD 2013
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