Avoiding tipping points in fisheries management through Gaussian Process Dynamic Programming

TL;DR

This paper introduces a Bayesian non-parametric approach using Gaussian Processes within a stochastic dynamic programming framework to better manage ecological systems at risk of tipping points, outperforming traditional model selection methods.

Contribution

It develops a novel Gaussian Process-based dynamic programming method that accounts for model uncertainty and limited data in ecological management, especially near tipping points.

Findings

GPDP outperforms standard model selection in simulations

Standard methods underestimate uncertainty leading to population collapse

GPDP maintains robust management without underestimating risks

Abstract

Model uncertainty and limited data are fundamental challenges to robust management of human intervention in a natural system. These challenges are acutely highlighted by concerns that many ecological systems may contain tipping points, such as Allee population sizes. Before a collapse, we do not know where the tipping points lie, if they exist at all. Hence, we know neither a complete model of the system dynamics nor do we have access to data in some large region of state-space where such a tipping point might exist. We illustrate how a Bayesian Non-Parametric (BNP) approach using a Gaussian Process (GP) prior provides a flexible representation of this inherent uncertainty. We embed GPs in a Stochastic Dynamic Programming (SDP) framework in order to make robust management predictions with both model uncertainty and limited data. We use simulations to evaluate this approach as compared with the standard approach of using model selection to choose from a set of candidate models. We find that model selection erroneously favors models without tipping points -- leading to harvest policies that guarantee extinction. The GPDP performs nearly as well as the true model and significantly outperforms standard approaches. We illustrate this using examples of simulated single-species dynamics, where the standard model selection approach should be most effective, and find that it still fails to account for uncertainty appropriately and leads to population crashes, while management based on the GPDP does not, since it does not underestimate the uncertainty outside of the observed data.