Aquat. Living Resour.
Volume 22, Number 2, April-June 2009
Fish Stock Assessments Using Surveys and Indicators
Page(s) 207 - 216
Published online 17 June 2009
  • Beare D.J., Needle C.L., Burns F., Reid, D.G., 2005, Using survey data independently from commercial data in stock assessment: An example using haddock in ICES Division VIa. ICES J. Mar. Sci. 62, 996–1005. [CrossRef] [Google Scholar]
  • Beverton R.J.H., Holt S.J., 1957, On the dynamics of exploited fish populations. UK Minist. Agric. Fish., Fish. Invest. (Ser. 2), 19, 533 p. [Google Scholar]
  • Cook R.M., 1997, Stock trends in six North Sea stocks as revealed by an analysis of research vessel surveys. ICES J. Mar. Sci. 54, 924–933. [CrossRef] [Google Scholar]
  • Cook R.M., 2004, Estimation of the age-specific rate of natural mortality for Shetland sandeels. ICES J. Mar. Sci. 61, 159–164. [CrossRef] [Google Scholar]
  • Cotter A.J.R., 2001, Intercalibration of North Sea International Bottom Trawl Surveys by fitting year-class curves. ICES J. Mar. Sci. 58, 622–632 [Erratum, Ibid. 58, 1340]. [CrossRef] [Google Scholar]
  • Cotter A.J.R., Buckland S.T., 2004, Using the EM algorithm to weight data sets of unknown precision when modeling fish stocks. Math. Biosci. 190, 1–7. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • Cotter J., Mesnil B., Piet, G., 2007, Estimating stock parameters from trawl CPUE-at-age series using year-class curves. ICES J. Mar. Sci. 63, 234–247. [Google Scholar]
  • Cotter A.J.R., Petitgas P., Abella A., Apostolaki P., Mesnil B., Politou C.-Y., Rivoirard J., Rochet M.J., Spedicato M., Trenkel V.M., Woillez M., 2009, Towards an ecosystem approach to fisheries management (EAFM) when trawl surveys provide the main source of information. Aquat. Living Resour. 22, 243–254. [CrossRef] [EDP Sciences] [Google Scholar]
  • Darby C.D., Flatman S., 1994, Virtual population analysis: version 3.1 (Windows/DOS) user guide. CEFAS, Lowestoft, UK. Information Technol. Ser. N° 1. [Google Scholar]
  • Deriso R.B., Quinn T.J.II, Neal, P.R., 1985, Catch-age analysis with auxiliary information. Can. J. Fish. Aquat. Sci. 42, 815–824. [CrossRef] [Google Scholar]
  • Fryer R.J., 2002, TSA: is it the way? Appendix D in Report of Working Group on Methods of Fish Stock Assessment, Dec. 2001. ICES CM 2002/D:01, 86–93. [Google Scholar]
  • Fournier D., 2005, An introduction to AD MODEL BUILDER version 7.0.1 for use in nonlinear modeling and statistics. Available from [Google Scholar]
  • Gudmundsson G., 1986, Statistical considerations in the analysis of catch-at-age observations. J. Cons. Internat. Explor. Mer 43, 83–90. [Google Scholar]
  • Gudmundsson G., 1994, Time series analysis of catch-at-age observations. Appl. Stat. 43, 117–126. [Google Scholar]
  • Gudmundsson G., 2004, Time-series analysis of abundance indices of young fish. ICES J. Mar. Sci. 61, 176–183. [CrossRef] [Google Scholar]
  • Hilborn R., Walters C.J., 1992, Quantitative fisheries stock assessment: Choice, dynamics and uncertainty. New York, Chapman and Hall. [Google Scholar]
  • Hillary R., 2009, An introduction to FLR fisheries simulation tools. Aquat. Living Resour. 22, 225–232. [CrossRef] [EDP Sciences] [Google Scholar]
  • Johnson S.J., Quinn T.J. II, 1987, Length frequency analysis of sablefish in the Gulf of Alaska. Technical Report UAJ-SFS-8714, University of Alaska, School of Fisheries and Science, Juneau, Alaska. Contract report to Auke Bay National Laboratory. [Google Scholar]
  • Needle C.L., Hillary R., 2007, Estimating uncertainty in nonlinear models: applications to survey-based assessments. ICES CM 2007/O:36. [Google Scholar]
  • NRC, 1998, Improving fish stock assessments. Washington, D.C., National Academy Press. (Appendix E describes the data generation; Appendix I shows plots of biomass trajectories). [Google Scholar]
  • Patterson K.R., Melvin G.D., 1996, Integrated Catch At Age Analysis Version 1:2. Scottish Fisheries Research Report. FRS: Aberdeen. [Google Scholar]
  • Pope J.G., Shepherd J.G., 1982, A simple method for the consistent interpretation of catch-at-age data. J. Cons. Internat. Explor. Mer 40, 176–184. [Google Scholar]
  • Quinn T.J.II, Deriso R.B., 1999, Quantitative Fish Dynamics. Oxford, Oxford University Press. [Google Scholar]
  • Skaug, H.J., Fournier, D.A., 2006, Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models. Comput. Stat. Data Anal. 51, 699–709. [Google Scholar]
  • Trenkel V.M., 2007, A biomass random effects model (BREM) for stock assessment using only survey data: application to Bay of Biscay anchovy. ICES CM 2007/O:03. [Google Scholar]
  • Trenkel V.M., 2008, A two-stage biomass random effects model for stock assessment without catches: What can be estimated using only biomass survey indices? Can. J. Fish. Aquat. Sci. 65, 1024–1035. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.