Abstract
We apply an artificial neural network (ANN) to predict recruitment and biomass development of Northeast Arctic cod. The ANN is trained using a genetic algorithm with input time series such as spawning stock biomass of cod, herring and capelin biomass, and temperature. Forecasts were made by training the ANN on parts of the time series (training set), and then using a trained ANN to predict cod recruitment or biomass in years outside of the training set. In general the predictions corresponded well to observations. The correlation (r2) between observed and predicted stock recruitment at age 3 was 0.74, based on a model with temperature, spawning stock biomass, and capelin biomass. The correlation between observed and predicted stock biomass was 0.89, 0.72 and 0.57 for one, two and three year predictions respectively. The best model for the one year predictions was based on input information on cod biomass, temperature, and cod landings. These results illustrate the strong forecasting ability of ANN models. In the light of our findings we discuss the potential benefit of applying ANN models as a forecasting technology in fisheries assessment.
Highlights
A prominent problem in fisheries assessment is to provide prognoses of fish stock development.Estimates of stock development rely on current stock abundance and expectations about future recruitment, individual growth and mortality rates, including fishing mortality
The training set increases from the start to the end of the time series, and for predictions made at the end of the time series, the reduced training set is equal to the full training set
Several other studies have pointed to the importance of temperature in explaining recruitment in Northeast Arctic (NA) cod (Nakken, 1994; Ottersen and Sundby, 1995; Brander, 2000; Sundby, 2000; Dippner and Ottersen, 2001)
Summary
A prominent problem in fisheries assessment is to provide prognoses of fish stock development.Estimates of stock development rely on current stock abundance and expectations about future recruitment, individual growth and mortality rates, including fishing mortality. The approach can be used to find patterns in complex data, and has been applied successfully in fisheries science for predicting yields of the Japanese sardine population (Komatsu et al, 1994; Aoki and Komatsu, 1997), yields of African lake fisheries (Laë et al, 1999), capelin biomass (Huse and Gjøsæter, 1999), and recruitment of Pacific herring (Chen and Ware, 1999). Under this approach, relevant input data are associated with the target variable, for example fish biomass in the subsequent year.
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