A Nonlinear Model for Predicting Interannual Changes in Calanus finmarchicus Abundance in the Gulf of Maine

Time series of physical and biological properties of the ocean are a valuable resource for developing models for ecological forecasting and ecosystem-based management. Both the physics of the oceans and organisms living in it can exhibit nonlinear dynamics. We describe the development of a nonlinear model that predicts the abundance of the important zooplankton species Calanus finmarchicus from hydrographic data from the Gulf of Maine. The results of a neural network model, including model diagnostics and forecasts, are presented. The best neural network model based on generalized cross-validation includes variables of C. finmarchicus abundance, herring abundance, and the state of the Gulf of Maine waters, with meaningful time lags. Forecasts are constructed for the model fit to 1978–2003 bimonthly data and corresponding forecasts intervals are obtained by the stationary bootstrap.