A framework for assessing the skill and value of operational recruitment forecasts

Forecasting variation in the recruitment to fish stocks is one of themost challenging and long-running problems in fisheries science and essentially remains unsolved today. Traditionally, recruitment forecasts are developed and evaluated based on explanatory and goodness-of-fit approaches that do not reflect their ability to predict beyond the data on which they were developed. Here, we propose a new generic framework that allows the skill and value of recruitment forecasts to be assessed in a manner that is relevant to their potential use in an operational setting. We assess forecast skill based on predictive power using a retrospective forecasting approach inspired by meteorology, and emphasize the importance of assessing these forecasts relative to a baseline. We quantify the value of these forecasts using an economic cost-loss decision model that is directly relevant to many forecast users. We demonstrate this framework using four stocks of lesser sandeel (Ammodytes marinus) in theNorth Sea, showing for the first time in an operationally realistic setting that skilful and valuable forecasts are feasible in two of these areas. This result shows the ability to produce valuable short-termrecruitment forecasts, and highlights the need to revisit our approach to and understanding of recruitment forecasting.