Evaluation of alternative age-based methods for estimating relative abundance from survey data in relation to assessment models

Fisheries Research

Volumn 151, Issue , 2014, Pages 91-99

  Berg, Casper W ^a   Nielsen, Anders ^a   Kristensen, Kasper ^a  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

Age based stock assessment; Generalized additive models; Survey Indices

Indexed keywords

EID: 84893058301     PISSN: 01657836     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fishres.2013.10.005     Document Type: Article

References (40)

1. Adlerstein S., Ehrich S. Patterns in diel variation of cod catches in north sea bottom trawl surveys. Fisheries Research 2003, 63:169-178.
2. Beare D., Needle C., Burns F., Reid D. Using survey data independently from commercial data in stock assessment: an example using haddock in ICES Division VIa. ICES Journal of Marine Science: Journal du Conseil 2005, 62:996-1005. http://icesjms.oxfordjournals.org/content/62/5/996.full.pdf+html.
3. Berg C.W., Kristensen K. Spatial age-length key modelling using continuation ratio logits. Fisheries Research 2012, 129-130:119-126.
4. Brynjarsdóttir J., Stefánsson G. Analysis of cod catch data from Icelandic groundfish surveys using generalized linear models. Fisheries Research 2004, 70:195-208.
5. Cadigan N., Chen J. Kernel regression estimators for nonparametric model calibration in survey sampling. Journal of Statistical Theory and Practice 2010, 4:1-25.
6. Cadigan N., Tobin J. Estimating the negative binomial dispersion parameter with highly stratified surveys. Journal of Statistical Planning and Inference 2010, 140:2138-2147.
7. Cadigan N.G. Confidence intervals for trawlable abundance from stratified-random bottom trawl surveys. Canadian Journal of Fisheries and Aquatic Sciences 2011, 68:781-794. http://www.nrcresearchpress.com/doi/pdf/10.1139/f2011-026.
8. Candy S. Modelling catch and effort data using generalised linear models, the tweedie distribution, random vessel effects and random stratum-by-year effects. CCAMLR Science 2004, 11:59-80.
9. Cervino S., Saborido-Rey F. Using the bootstrap to investigate the effects of varying tow lengths and catch sampling schemes in fish survey. Fisheries Research 2006, 79:294-302.
10. Chen J., Thompson M.E., Wu C. Estimation of fish abundance indices based on scientific research trawl surveys. Biometrics 2004, 60:116-123.
11. Dunn P.K., Smyth G.K. Series evaluation of Tweedie exponential dispersion model densities. Statistics and Computing 2005, 15:267-280.
12. Folmer O., Pennington M. A statistical evaluation of the design and precision of the shrimp trawl survey off West Greenland. Fisheries Research 2000, 49:165-178.
13. Fournier D.A., Skaug H.J., Ancheta J., Sibert J., Ianelli J., Magnusson A., Maunder M.N., Nielsen A. AD model builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optimization Methods and Software 2012, 27:233-249.
14. ICES, 2010. Manual for the International Bottom Trawl Surveys Revision VIII.
15. ICES, 2012. Report of the Herring Assessment Working Group for the Area South of 62 deg N (HAWG). ICES Document CM 2012/ACOM:06.
16. ICES, 2012. Report of the Working Group on the Assessment of Demersal Stocks in the North Sea and Skagerrak (WGNSSK). ICES Document CM 2012/ACOM:13.
17. ICES, 2012. Report of the Workshop on Implementation in DATRAS of Confidence Limits Estimation of, 10-12 May 2006. ICES Headquarters, Copenhagen.
18. Kimura D.K., Somerton D.A. Review of statistical aspects of survey sampling for marine fisheries. Reviews in Fisheries Science 2006, 14:245-283.
19. Kristensen, K., Berg, C.W., 2012. DATRAS package for R. http://rforge.net/DATRAS/.
20. Kristensen K., Lewy P., Beyer J.E. How to validate a length-based model of single-species fish stock dynamics. Canadian Journal of Fisheries and Aquatic Sciences 2006, 63:2531-2542. http://www.nrcresearchpress.com/doi/pdf/10.1139/f06-135.
21. Lewy P., Kristensen K. Modelling the distribution of fish accounting for spatial correlation and overdispersion. Canadian Journal of Fisheries and Aquatic Sciences 2009, 66:1809-1820.
22. Maunder M., Starr P. Fitting fisheries models to standardised CPUE abundance indices. Fisheries Research 2003, 63:43-50.
23. Maunder M.N., Punt A.E. Standardizing catch and effort data: a review of recent approaches. Fisheries Research 2004, 70:141-159.
24. Maxwell D.L., Armstrong M.J., Beggs S., Aldridge J.N. Annual egg production estimates of cod (Gadus morhua), plaice (Pleuronectes platessa) and haddock (Melanogrammus aeglefinus) in the irish sea: the effects of modelling choices and assumptions. Fisheries Research 2012, 117-118:146-155. Egg Production Methods in Marine Fisheries.
25. Myers R.A., Cadigan N.G. Statistical analysis of catch-at-age data with correlated errors. Canadian Journal of Fisheries and Aquatic Sciences 1995, 52:1265-1273.
26. Myers R.A., Pepin P. The robustness of lognormal-based estimators of abundance. Biometrics 1990, 46. 1185-1192+.
27. Oehlert G.W. A Note on the Delta Method. American Statistician 1992, 46:27-29.
28. Pennington M. Efficient estimators of abundance, for fish and plankton surveys. Biometrics 1983, 39.
29. Pennington M. Estimating the mean and variance from highly skewed marine data. Fishery Bulletin (Washington D C) 1996, 94:498-505.
30. Petrakis G., MacLennan D.N., Newton A.W. Day-night and depth effects on catch rates during trawl surveys in the North Sea. ICES Journal of Marine Science 2001, 58:50-60.
31. Piet G.J. Using external information and GAMs to improve catch-at-age indices for North Sea plaice and sole. ICES Journal of Marine Science: Journal du Conseil 2002, 59:624-632. http://icesjms.oxfordjournals.org/content/59/3/624.full.pdf+html.
32. R Development Core Team, 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. ISBN 3-900051-07-0.
33. Shono H. Application of the Tweedie distribution to zero-catch data in CPUE analysis. Fisheries Research 2008, 93:154-162.
34. Smith S.J. Use of statistical models for the estimation of abundance from groundfish trawl survey data. Canadian Journal of Fisheries and Aquatic Sciences 1990, 47:894-903.
35. Stefansson G. Analysis of groundfish survey abundance data: combining the GLM and delta approaches. ICES Journal of Marine Science 1996, 53:577-588.
36. Tweedie M.C.K. An index which distinguishes between some important exponential families. Statistics: Applications and New Directions. Proceedings of the Indian Statistical Institute Golden Jubilee International Conference 1984, 579-604. Indian Statistical Institute, Calcutta. J.K. Ghosh, J. Roy (Eds.).
37. Walters C., Punt A. Placing odds on sustainable catch using virtual population analysis and survey data. Canadian Journal of Fisheries and Aquatic Sciences 1994, 51:946-958.
38. Wood, S., 2006. Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC.
39. Wood S.N. On confidence intervals for generalized additive models based on penalized regression splines. Australian & New Zealand Journal of Statistics 2006, 48:445-464.
40. Wood S.N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2011, 73:3-36.