Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-28T20:54:10.377Z Has data issue: false hasContentIssue false
  • English
  • Français

A review of fishery-independent assessment models, and initial evaluation based on simulated data

Published online by Cambridge University Press:  17 June 2009

Benoit Mesnil ,
John Cotter ,
Rob J. Fryer ,
Coby L. Needle  and
Verena M. Trenkel
Benoit Mesnil
Affiliation:
Ifremer, Département EMH, BP 21105, 44311 Nantes Cedex 3, France
John Cotter
Affiliation:
CEFAS Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk NR33 0HT, UK
Rob J. Fryer
Affiliation:
FRS Marine Laboratory, PO Box 101, Victoria Road, Aberdeen AB11 9DB, UK
Coby L. Needle
Affiliation:
FRS Marine Laboratory, PO Box 101, Victoria Road, Aberdeen AB11 9DB, UK
Verena M. Trenkel
Affiliation:
Ifremer, Département EMH, BP 21105, 44311 Nantes Cedex 3, France
Get access

Abstract

Large uncertainties in catch data (officially-reported landings and discards) are undermining the ability of scientific organisations to provide valid management advice based on the conventional approach of analytical stock assessments. There is thus an urgent need to consider alternative tools that do not depend on long series of precise age-structured catch data. This paper presents four fishery-independent assessment models developed under the EU project FISBOAT (Fishery Independent Survey Based Operational Assessment Tools). It also reports on rudimentary tests based on simulated data, using the same data sets and protocol as an evaluation study conducted by the US National Research Council in 1997. The survey-based assessment models at hand are able to reliably capture the major signal in biomass and recruitment, although they smooth out transient changes. However, they cannot provide absolute abundance estimates, only relative values on an arbitrary scale. The survey-based approaches could provide more rapid updates of the state of stocks than catch-based methods.

Keywords

Fish stock assessmentCatch-free modelsFishery-independent assessment methods (F-I methods)Survey indices
Type
Research Article
Information
Aquatic Living Resources , Volume 22 , Issue 2: Fish Stock Assessments Using Surveys and Indicators , April 2009 , pp. 207 - 216
Copyright
© EDP Sciences, IFREMER, IRD, 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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
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.
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
Cook, R.M., 2004, Estimation of the age-specific rate of natural mortality for Shetland sandeels. ICES J. Mar. Sci. 61, 159–164. CrossRef
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
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
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.
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
Darby C.D., Flatman S., 1994, Virtual population analysis: version 3.1 (Windows/DOS) user guide. CEFAS, Lowestoft, UK. Information Technol. Ser. N° 1.
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
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.
Fournier D., 2005, An introduction to AD MODEL BUILDER version 7.0.1 for use in nonlinear modeling and statistics. Available from http://otter-rsch.com/admodel.htm.
Gudmundsson, G., 1986, Statistical considerations in the analysis of catch-at-age observations. J. Cons. Internat. Explor. Mer 43, 83–90. CrossRef
Gudmundsson, G., 1994, Time series analysis of catch-at-age observations. Appl. Stat. 43, 117–126. CrossRef
Gudmundsson, G., 2004, Time-series analysis of abundance indices of young fish. ICES J. Mar. Sci. 61, 176–183. CrossRef
Hilborn R., Walters C.J., 1992, Quantitative fisheries stock assessment: Choice, dynamics and uncertainty. New York, Chapman and Hall.
Hillary, R., 2009, An introduction to FLR fisheries simulation tools. Aquat. Living Resour. 22, 225–232. CrossRef
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.
Needle C.L., Hillary R., 2007, Estimating uncertainty in nonlinear models: applications to survey-based assessments. ICES CM 2007/O:36.
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).
Patterson K.R., Melvin G.D., 1996, Integrated Catch At Age Analysis Version 1:2. Scottish Fisheries Research Report. FRS: Aberdeen.
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. CrossRef
Quinn T.J.II, Deriso R.B., 1999, Quantitative Fish Dynamics. Oxford, Oxford University Press.
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. CrossRef
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.
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