Forecasting for Recreational Fisheries Management: What's the Catch?

Abstract

AbstractThe Magnuson–Stevens Fishery Conservation and Management Reauthorization Act of 2006 required regional fishery management councils to implement annual catch limits (ACLs) for nearly all stocks under U.S. federal management. Since 2011, the number of stocks requiring ACLs (and monitoring) has increased nearly 10‐fold, with strict accountability measures requiring either in‐season quota closures or shortening of subsequent seasons to avoid ACL overages. Robust forecasts of landings can also provide a projected baseline for evaluation of proposed management alternatives. We compared generalized linear models (GLMs), generalized additive models (GAMs), and seasonal autoregressive integrated moving average (SARIMA) models in terms of fit, accuracy, and ability to forecast landings of four representative fish stocks that support recreational fisheries in the southeastern United States. All models were useful in developing reliable forecasts to inform management. The GAMs provided the best fit to the observed data; however, the modeling approaches of the SARIMA model and GLM provided the best forecasts for most scenarios. The SARIMA model and GLM also provided the best predictions of the seasonal trend in landings, a desirable feature for in‐season quota monitoring. The SARIMA model was more sensitive and the GLM was less sensitive to recent trends, providing a useful “bookend” for forecasts. The time span of input data affected forecast accuracy from all model types considered. This study suggests multiple forecasting models should be investigated and performance metrics carefully selected and evaluated, as no single model is likely to perform best for all stocks of interest.Received December 18, 2014; accepted April 20, 2015