Empirical dynamic programming for model‐free ecosystem‐based management

Abstract

Abstract Quantitative ecosystem‐based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best‐studied systems. Here, we develop a management scheme that is based on predictive models driven by the observed dynamics. We show that near‐optimal management policies can be constructed from time‐series data by merging empirical dynamic modelling and stochastic dynamic programming. The Empirical Dynamic Programming approach performs well in cases we examined and outperformed a commonly used single‐species alternative. We expect model‐free ecosystem‐based management to be of use wherever ecosystem dynamics are uncertain or observations of the system do not cover all relevant species.

Keywords

• approximate dynamic programming
• ecosystem management
• Gaussian process regression
• nonlinear methods
• temporal difference learning
• time‐delay embedding