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dc.contributor.authorKell, Laurence T.
dc.contributor.authorPilling, Graham
dc.contributor.authorKirkwood, Geoffrey P.
dc.contributor.authorPastoors, Martin
dc.contributor.authorAbaunza, Pablo
dc.contributor.authorAps, Robert
dc.contributor.authorBiseau, Alain
dc.contributor.authorKorsbrekke, Knut
dc.contributor.authorKunzlik, Philip
dc.contributor.authorLaurec, Alain
dc.contributor.authorMesnil, Benoit
dc.contributor.authorNeedle, Coby L.
dc.contributor.authorRoel, Beatriz
dc.contributor.authorUlrich, Clara
dc.date.accessioned2012-11-06T20:08:53Z
dc.date.available2012-11-06T20:08:53Z
dc.date.issued2003
dc.identifier.citationThis report is not to be cited without prior reference to the authorsno_NO
dc.identifier.urihttp://hdl.handle.net/11250/106505
dc.description.abstractThis study evaluated through simulation management strategy that stabilise catch levels by setting bounds on the inter-annual variability in Total Allowable Catches (TACs). An integrated modelling approach was used, which modelled both the ‘real’ and observed systems and the interactions between all system components. The modelling framework therefore allowed evaluation of the robustness of candidate management strategies to both the intrinsic properties of the systems, and the ability to observe, monitor, assess and control them. Strategies were evaluated in terms of level of risk (measured as the probability of spawning stock biomass falling below the biomass limit reference level for the stock) and cumulative yield. The simulation approach used provides a powerful tool for the examination of the performance of candidate management strategies. It has shown that better management is not necessarily going to be achieved by improving the assessement, since even with a perfect assessment (where the simulated working group knew stock status perfectly) stocks may crash at fishing levels that standard stochastic projections would suggest were safe. Also explicitly modelling the assessment process can result in quite different outcomes than those predicted by the simple projection traditionally used by stock assessment working groups. This is because the simple projection assumes that the status of the stock in the current year is known without error and that the target fishing mortality can be achieved without error. However, in practice the assessment is based on last years data and the effect of any management measure on SSB is only manifest, following the implementation of the quota, at the end of the following year. The choice of target and fishing mortality levels and minimum stock levels results from ICES interpretation of the precautionary approach. This lead to the definition of fishing mortality and biomass reference points that are intended to prevent over-fishing and to trigger recovery plans when a stock is overfished respectively. Although, fishing mortality and biomass reference points were originally intended to be independent, a fishing mortality level implies a corresponding biomass level. In the case of saithe a fishing mortality of 0.40 (i.e. the FPA level) would drive the stock to Blim, suggesting that the choice of biomass and target reference points are not consistent for this stock.no_NO
dc.language.isoengno_NO
dc.publisherICESno_NO
dc.relation.ispartofseriesICES CM Documents;2003/X:7
dc.subjectcodno_NO
dc.subjecttorskno_NO
dc.subjecthaddockno_NO
dc.subjecthyseno_NO
dc.subjectwhitingno_NO
dc.subjecthvittingno_NO
dc.subjectsaitheno_NO
dc.subjectseino_NO
dc.subjecthakeno_NO
dc.subjectlysingno_NO
dc.subjectcatch strategyno_NO
dc.subjectfangststrategino_NO
dc.subjectstock assessmentno_NO
dc.subjectbestandsberegningno_NO
dc.subjectmanagement adviceno_NO
dc.subjectforvaltningsrådno_NO
dc.titleLimiting inter-annual variation in total allowable catch strategies. An application to ICES roundfish stocksno_NO
dc.typeWorking paperno_NO
dc.subject.nsiVDP::Agriculture and fishery disciplines: 900::Fisheries science: 920::Catch: 925no_NO
dc.subject.nsiVDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452no_NO
dc.subject.nsiVDP::Agriculture and fishery disciplines: 900::Fisheries science: 920::Fish health: 923no_NO
dc.subject.nsiVDP::Social science: 200::Library and information science: 320::Knowledge retrieval and organization: 323no_NO
dc.source.pagenumber21 s.no_NO


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