Managing a complex population structure: exploring the importance of information from fisheries-independent sources. Hintzen, N. T., Roel, B., Benden, D., Clarke, M., Egan, A., Nash, R. D. M., Rohlf, N., & Hatfield, E. M. C. ICES JOURNAL OF MARINE SCIENCE, 72(2):528-542, OXFORD UNIV PRESS, GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND, JAN-FEB, 2015.
doi  abstract   bibtex   
Natural resource managers aim to manage fish stocks at sustainable levels. Often, management of these stocks is based on the results of analytical stock assessments. Accurate catch data, which can be attributed to a specific population unit and reflects the population structure, are needed for these approaches. Often though, the quality of the catch data is compromised when dealing with a complex population structure where fish of different population units mix in a fishery. The herring population units west of the British Isles are prone to mixing. Here, the inability to perfectly allocate the fish caught to the population unit they originate from, due to classification problems, poses problems for management. These mixing proportions are often unknown; therefore, we use simulation modelling combined with management strategy evaluation to evaluate the role fisheries-independent surveys can play in an assessment to provide unbiased results, irrespective of population unit mixing and classification success. We show that failure to account for mixing is one of the major drivers of biased estimates of population abundance, affecting biomass reference points and MSY targets. When mixing of population units occurs, the role a survey can play to provide unbiased assessment results is limited. Either different assessment models should be employed or stock status should be considered from the survey data alone. In addition, correctly classifying the origin of fish is especially important for those population units that are markedly smaller in size than other units in the population complex. Without high classification success rates, smaller population units are extremely vulnerable to overexploitation.
@article{ ISI:000350154300020,
Author = {Hintzen, N. T. and Roel, B. and Benden, D. and Clarke, M. and Egan, A.
   and Nash, R. D. M. and Rohlf, N. and Hatfield, E. M. C.},
Title = {{Managing a complex population structure: exploring the importance of
   information from fisheries-independent sources}},
Journal = {{ICES JOURNAL OF MARINE SCIENCE}},
Year = {{2015}},
Volume = {{72}},
Number = {{2}},
Pages = {{528-542}},
Month = {{JAN-FEB}},
Abstract = {{Natural resource managers aim to manage fish stocks at sustainable
   levels. Often, management of these stocks is based on the results of
   analytical stock assessments. Accurate catch data, which can be
   attributed to a specific population unit and reflects the population
   structure, are needed for these approaches. Often though, the quality of
   the catch data is compromised when dealing with a complex population
   structure where fish of different population units mix in a fishery. The
   herring population units west of the British Isles are prone to mixing.
   Here, the inability to perfectly allocate the fish caught to the
   population unit they originate from, due to classification problems,
   poses problems for management. These mixing proportions are often
   unknown; therefore, we use simulation modelling combined with management
   strategy evaluation to evaluate the role fisheries-independent surveys
   can play in an assessment to provide unbiased results, irrespective of
   population unit mixing and classification success. We show that failure
   to account for mixing is one of the major drivers of biased estimates of
   population abundance, affecting biomass reference points and MSY
   targets. When mixing of population units occurs, the role a survey can
   play to provide unbiased assessment results is limited. Either different
   assessment models should be employed or stock status should be
   considered from the survey data alone. In addition, correctly
   classifying the origin of fish is especially important for those
   population units that are markedly smaller in size than other units in
   the population complex. Without high classification success rates,
   smaller population units are extremely vulnerable to overexploitation.}},
Publisher = {{OXFORD UNIV PRESS}},
Address = {{GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Hintzen, NT (Reprint Author), IMARES, Inst Marine Resources \& Ecosyst Studies, POB 68, NL-1970 AB Ijmuiden, Netherlands.
   Hintzen, N. T.; Benden, D., IMARES, Inst Marine Resources \& Ecosyst Studies, NL-1970 AB Ijmuiden, Netherlands.
   Roel, B., Cefas Lab, Lowestoft NR33 0HT, Suffolk, England.
   Clarke, M.; Egan, A., Inst Marine, Oranmore, Galway, Ireland.
   Nash, R. D. M., Inst Marine Res, N-5817 Bergen, Norway.
   Rohlf, N., Thunen Inst Sea Fisheries, D-22767 Hamburg, Germany.
   Hatfield, E. M. C., Marine Scotland Sci, Marine Lab, Aberdeen AB11 9DB, Scotland.}},
DOI = {{10.1093/icesjms/fsu102}},
ISSN = {{1054-3139}},
EISSN = {{1095-9289}},
Keywords = {{Atlantic Herring; British Isles; classification; Clupea harengus; FLR;
   management strategy evaluation; mixing; scientific survey; stock
   structure}},
Keywords-Plus = {{HERRING CLUPEA-HARENGUS; STATE-SPACE MODEL; NORTH-SEA; STOCK ASSESSMENT;
   MANAGEMENT STRATEGIES; SPAWNING COMPONENTS; BRITISH-ISLES; CELTIC SEA;
   DYNAMICS; ATLANTIC}},
Research-Areas = {{Fisheries; Marine \& Freshwater Biology; Oceanography}},
Web-of-Science-Categories  = {{Fisheries; Marine \& Freshwater Biology; Oceanography}},
Author-Email = {{niels.hintzen@wur.nl}},
Funding-Acknowledgement = {{EU {[}MARE/2011/16 Lot 1]}},
Funding-Text = {{We thank Daniel Goethel and an anonymous reviewer for their helpful
   comments on earlier versions of this manuscript. This research was
   supported through the EU Open call for tenders No MARE/2011/16 Lot 1.
   The article does not necessarily reflect the views of the European
   Commission and does not anticipate the Commission's future policy in
   this area.}},
Number-of-Cited-References = {{58}},
Times-Cited = {{2}},
Usage-Count-Last-180-days = {{3}},
Usage-Count-Since-2013 = {{17}},
Journal-ISO = {{ICES J. Mar. Sci.}},
Doc-Delivery-Number = {{CC2DC}},
Unique-ID = {{ISI:000350154300020}},
OA = {{No}},
DA = {{2017-08-17}},
}
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