Bayesian modelling of catch in a north-west Atlantic fishery

• Published in: Journal of the Royal Statistical Society Series C: Applied Statistics
• Main Authors: Fernández-Llana, Carmen, Ley, Eduardo, Steel, Mark F.J.
• Other Authors: European Commission
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley-Blackwell 2002
• Subjects: Bayesian model averaging; Categorical variables; Grand Bank fishery; Predictive inference; Probit model; North West Atlantic; Northwest Atlantic
• Online Access: http://hdl.handle.net/10261/342029; https://doi.org/10.1111/1467-9876.00268; https://doi.org/10.13039/501100000780; https://api.elsevier.com/content/abstract/scopus_id/0036426783

We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used and the mesh size of the nets) are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and four categorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log-catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to the prediction of catches for single and aggregated ships. Carmen Fernández and Mark Steel were also affiliated to the Center for Economic Research and the Department of Econometrics, Tilburg University, the Netherlands, during much of this work, where Carmen Fernández was supported by training and mobility of researchers grant ERBFMBICT 961021 awarded by the European Commission. Peer reviewed