Modelling stock–recruitment relationships to examine stock management policies

Simulation studies are widely used for fish stock management. In these studies, the forecasting of 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 specifically 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 to the new model using leave-one-out cross-validation for 61 datasets from ICES, the Japanese Fisheries Agency, and PICES. The new algorithm had the smallest prediction error for 49 of the datasets, but was slightly biased by the precautionary treatment of high recruitment predictions