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dc.contributor.author Boettiger, Carl
dc.contributor.author Mangel, Marc
dc.contributor.author Munch, Stephan
dc.date.accessioned 2015-01-12T22:47:14Z
dc.date.available 2015-01-12T22:47:14Z
dc.date.issued 2015-01-07
dc.identifier doi:10.5061/dryad.mj226
dc.identifier.citation Boettiger C, Mangel M, Munch S (2015) Data from: Avoiding tipping points in fisheries management through Gaussian process dynamic programming. Proceedings of the Royal Society B 282(1801): 20141631.
dc.identifier.uri http://hdl.handle.net/10255/dryad.74204
dc.description 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.
dc.relation.haspart doi:10.5061/dryad.mj226/1
dc.relation.haspart doi:10.5061/dryad.mj226/2
dc.relation.haspart doi:10.5061/dryad.mj226/3
dc.relation.haspart doi:10.5061/dryad.mj226/4
dc.relation.isreferencedby doi:10.1098/rspb.2014.1631
dc.relation.isreferencedby PMID:25567644
dc.title Data from: Avoiding tipping points in fisheries management through Gaussian process dynamic programming
dc.type Article
prism.publicationName Proceedings of the Royal Society B
dryad.dansTransferDate 2018-04-20T09:13:08.648+0000
dryad.dansEditIRI https://easy.dans.knaw.nl/sword2/container/0df206f0-d1ff-4ed3-a25b-f2d1c3e05a3d
dryad.dansArchiveDate 2018-04-23T13:26:28.670+0000

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