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ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on May 15, 2007
ICES Journal of Marine Science: Journal du Conseil 2007 64(5):870-877; doi:10.1093/icesjms/fsm054
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© 2007 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Modelling stock–recruitment relationships to examine stock management policies

A. Kimoto1,, T. Mouri1 and T. Matsuishi2

1 Graduate School of Fisheries Sciences, Hokkaido University, 3-1-1 Minato-cho Hakodate Hokkaido, 041-8611 Japan
2 Faculty of Fisheries Sciences, Hokkaido University, 3-1-1 Minato-cho Hakodate Hokkaido, 041-8611 Japan

Correspondence to A. Kimoto: tel: +81 138 405585; fax: +81 138 408857; e-mail: ai{at}fish.hokudai.ac.jp

Kimoto, A., Mouri, T., and Matsuishi, T. 2007. Modelling stock–recruitment relationships to examine stock management policies. – ICES Journal of Marine Science, 64: 870–877.

Simulation studies are used widely for fish stock management. In such studies, forecasting future recruitment, which can vary greatly between years, has become an essential part of evaluating management strategies. We propose a new forecasting algorithm to predict recruitment for short- or medium-term stochastic projections, using a stock–recruitment relationship. We address cases in which the spawning stock has dropped below previously observed levels, or in which predicted recruitment is situated close to the maximum observed level. The relative prediction error of seven existing algorithms was compared with that of the new model using leave-one-out cross-validation for 61 data sets from ICES, the Japanese Fisheries Agency, and PICES. The new algorithm had the smallest prediction error for 49 of the data sets, but was slightly biased by the precautionary treatment of predictions of high recruitment.

Keywords: Beverton and Holt, non-parametric, operating model, recruitment prediction, Ricker, simulation, stock–recruitment relationship

Received 28 July 2006; accepted 14 March 2007; advance access publication 15 May 2007.


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