Quantifying the effects of dynamical noise on the predictability of a simple ecosystem model

The need to understand the effects of anthropogenic perturbations on ocean biology has renewed interest in ecosystem and biogeochemical models in recent years. We develop a nonlinear time series approach to quantify the effects of different types of noise on ecosystem dynamics. Different types of noise can alter the local predictability of the system, induce qualitative regime shifts in model dynamics, and destroy (create) internal nonlinear oscillations and chaos. The ecosystem model we use in our article is a model of plankton dynamics and nitrogen cycling. It is a compartmental model (NPZD) consisting of compartments for nitrogen (N), phytoplankton (P), zooplankton (Z), and detritus (D). The flows or intercompartmental exchanges are modeled as a nonlinear system of four first-order differential equations. The types of noise of interest are both independent and correlated. Because the noise is an integral part of the system's dynamics, a nonlinear time series approach is used to quantify the dynamics and predictability of the system. This involves fitting neural network models and estimating dynamical system quantities of interest such as global and local Lyapunov exponents, along with measures of uncertainty for these estimates. Copyright (C) 2004 John Wiley Sons, Ltd.