Forecasting Annual Harvests of Atlantic and Gulf Menhaden

Abstract

Continuous records of annual landings and fishing effort exist in the Atlantic purse-seine fishery for Atlantic menhaden Brevoortia tyrannus since 1940 and the Gulf of Mexico fishery for Gulf menhaden B. patronus since 1946. Currently, year-ahead forecasts of landings from these species-specific fisheries separated by the Florida peninsula are provided to the industry by means of multiple-linear-regression models that relate landings and effort over the data series. Here, we compare three methods for this purpose—multiple regression, time series, and artificial neural networks—to determine whether forecast accuracy can be increased. Best-fit models were developed with each method for each fishery, and then 10-year retrospective analyses of 1-year-ahead catch forecasts were compared among the three methods. In general, multiple-regression and artificial neural network models were similar in their fit to the data series and both were better than time series models, judging from the Akaike information criterion, the correlation between observed and predicted catches, the mean prediction error, and the root mean square error of prediction. A 10-year retrospective analysis (1993–2002) of 1-year-ahead catch forecasts indicates that the three methods provided similar within-stock mean absolute forecast errors (19–21% in the Atlantic and 15–20% in the Gulf), with generally better forecasts for the Gulf fishery. Overall, multiple-regression and artificial neural network models provide lower average catch forecast errors and better fits to the fishery data, whereas similar forecast errors are provided by a univariate time series model (autoregressive integrated moving average model) in the Atlantic and a multivariate time series model (state space model) in the Gulf.

Notes

¹The data are available as a digital file from the corresponding author.