title: 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 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 favours models without tipping points, leading to harvest policies that guarantee extinction. The Gaussian process dynamic programming (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, as it does not underestimate the uncertainty outside of the observed data.
authors.affiliation: Center for Stock Assessment Research, Department of Applied Math and Statistics, University of California, Mail Stop SOE-2, Santa Cruz, CA 95064, USA cboettig@gmail.com.
authors.forename: Carl
authors.identifier: http://orcid.org/0000-0002-1642-628X
authors.initials: C
authors.lastname: Boettiger
authors.affiliation: Center for Stock Assessment Research, Department of Applied Math and Statistics, University of California, Mail Stop SOE-2, Santa Cruz, CA 95064, USA.
authors.forename: Marc
authors.initials: M
authors.lastname: Mangel
authors.affiliation: Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, 110 Shaffer Road, Santa Cruz, CA 95060, USA.
authors.forename: Stephan
authors.initials: S
authors.lastname: Munch
mesh_terms: D001499:Bayes Theorem; D003247:Conservation of Natural Resources; D005398:Fisheries; D008954:Models, Biological; D016011:Normal Distribution; D011157:Population Dynamics; D035501:Uncertainty
keywords: Bayesian; Gaussian processes; decision theory; fisheries management; non-parametric optimal control; structural uncertainty