Generic management procedures for data-poor fisheries:forecasting with few data

 

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dc.contributor.author Geromont, Helena F
dc.contributor.author Butterworth, Doug S
dc.date.accessioned 2016-03-09T10:58:18Z
dc.date.available 2016-03-09T10:58:18Z
dc.date.issued 2015
dc.identifier http://dx.doi.org/10.1093/icesjms/fst232
dc.identifier.citation Geromont, H. F., & Butterworth, D. S. (2015). Generic management procedures for data-poor fisheries: forecasting with few data. ICES Journal of Marine Science: Journal du Conseil, 72(1), 251-261. en_ZA
dc.identifier.issn 1054-3139 en_ZA
dc.identifier.uri http://hdl.handle.net/11427/17612
dc.identifier.uri http://icesjms.oxfordjournals.org/content/early/2014/01/15/icesjms.fst232
dc.description.abstract The majority of fish stocks worldwide are not managed quantitatively as they lack sufficient data, particularly a direct index of abundance, on which to base an assessment. Often these stocks are relatively “low value”, which renders dedicated scientific management too costly, and a generic solution is therefore desirable. A management procedure (MP) approach is suggested where simple harvest control rules are simulation tested to check robustness to uncertainties. The aim of this analysis is to test some very simple “off-the-shelf” MPs that could be applied to groups of data-poor stocks which share similar key characteristics in terms of status and demographic parameters. For this initial investigation, a selection of empirical MPs is simulation tested over a wide range of operating models (OMs) representing resources of medium productivity classified as severely depleted, to ascertain how well these different MPs perform. While the data-moderate MPs (based on an index of abundance) perform somewhat better than the data-limited ones (which lack such input) as would be expected, the latter nevertheless perform surprisingly well across wide ranges of uncertainty. These simple MPs could well provide the basis to develop candidate MPs to manage data-limited stocks, ensuring if not optimal, at least relatively stable sustainable future catches. en_ZA
dc.language eng en_ZA
dc.publisher Oxford University Press en_ZA
dc.source ICES Journal of Marine Science en_ZA
dc.source.uri http://icesjms.oxfordjournals.org/
dc.title Generic management procedures for data-poor fisheries:forecasting with few data en_ZA
dc.type Journal Article en_ZA
dc.date.updated 2016-03-09T09:05:19Z
uct.type.publication Research en_ZA
uct.type.resource Article en_ZA
uct.subject.keywords Bayes en_ZA
uct.subject.keywords Data-poor en_ZA
uct.subject.keywords Generic en_ZA
uct.subject.keywords Management procedures en_ZA
uct.subject.keywords Simulations en_ZA
uct.subject.keywords Target/limit reference points en_ZA
uct.subject.keywords Uncertainty en_ZA
uct.subject.keywords Yield/risk trade-offs en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Marine Resource Assessment and Management Group en_ZA
uct.type.filetype Text
uct.type.filetype Image


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