Ecosystem based multi-species management using Empirical Dynamic Programming

Control theory and stochastic dynamic programming have long been used to develop optimal single-species management policies. However, most species interact with others through competition and predation as parts of complex ecosystems. As a consequence, it is unclear how far from optimal the single species policies currently in use actually are. Moreover, there are as yet no scalable algorithms for optimal ecosystem management. Here, we merge recently developed tools from machine learning and nonlinear dynamics to construct and evaluate near-optimal policies in multi-species systems. Specifically, a non-parametric model for the dynamics is estimated from time series data using Gaussian process-based dynamic modeling. A policy is then derived from the inferred dynamics using a temporal difference learning algorithm. Policy performance is benchmarked against single-species policies and the ad hoc ecosystem policies that have been previously offered. We found that EDP policies are closer to the true optimal policies than single-species policies in multi-species systems with two controls and three objectives. The Pareto fronts illustrate the flexibility of EDP policies compared with single-species policies.