Avoiding tipping points in fisheries management through Gaussian process dynamic programming.

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 approach using a Gaussian process (GP) prior provides a flexible representation of this inherent uncertainty. We embed GPs in a stochastic dynamic programming framework in order to make robust management predictions with both model u

SUBMITTER: Boettiger C 

PROVIDER: S-EPMC4308990 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature