Multi-level stock-recruit analysis: Beyond steepness and into model uncertainty

Ecological Modelling

Volumn 242, Issue , 2012, Pages 69-80

  Hillary, Richard M ^a   Levontin, Polina ^b   Kuikka, Sakari ^c   Manteniemi, Samu ^c   Mosqueira, Iago ^d   Kell, Laurie ^e  

^a CSIRO   (Australia)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

^c UNIVERSITY OF HELSINKI   (Finland)

^d JOINT RESEARCH CENTRE   (Italy)

^e International Commission for the Conservation of Atlantic Tunas   (Spain)

Author keywords

Hierarchical modelling; Model selection; Stock recruit analysis

Indexed keywords

INFORMATION CRITERION; KEY PARAMETERS; MODEL SELECTION; MODEL UNCERTAINTIES; PARAMETRIC UNCERTAINTIES; PREDICTIVE ANALYSIS; RETROSPECTIVE ANALYSIS; STOCK-RECRUIT ANALYSIS; SUSTAINABLE YIELDS; TEMPORAL INFORMATION;

ECOLOGY;

UNCERTAINTY ANALYSIS;

ECOLOGICAL MODELING; ESTIMATION METHOD; RECRUITMENT (POPULATION DYNAMICS); SEABIRD; SPAWNING; STOCK ASSESSMENT;

CLUPEIDAE;

EID: 84863461822     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2012.05.024     Document Type: Article

References (24)

1. Bernardo J.M. Bayesian statistics. Encyclopaedia of Life Support Systems (EOLSS): Probability and Statistics. 2003, Oxford University Press, London. R. Viertl (Ed.).
2. Brooks S.P., Roberts G.O. Assessing convergence of Markov chain Monte Carlo algorithms. Statistics and Computing 1998, 8:319-335.
3. Celeux G., Forbes F., Robert C.P., Titterington D.M. Deviance information criteria for missing data models. Bayesian Analysis 2006, 4:651-674.
4. Dorn M.W. Advice on west coast rockfish harvest rates from Bayesian meta-analysis of stock-recruit relationships. North American Journal of Fish Management 2002, 22:280-300.
5. Forrest R., McAllister M.K., Dorn M.W., Martell S., Stanley R. Hierarchical Bayesian estimation of productivity and reference points for Pacific rockfishes (Sebastes spp.) under alternative assumptions about the stock-recruit function. Canadian Journal of Fisheries and Aquatic Sciences 2010, 67:1611-1634.
6. Gelman A., Carlin B.J., Stern H.S., Rubin R.B. Bayesian Data Analysis 1995, Chapman and Hall, London.
7. Geman S., Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 1984, 6:721-741.
8. Gilks W.R., Richardson S., Spiegelhalter D.J. Markov chain Monte Carlo in practice 1996, Chapman and Hall, London.
9. Hilborn R., Walters C.J. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty 1992, Chapman and Hall, London. 1st ed.
10. Hillary R.M. A new method for estimating growth transition matrices. Biometrics 2011, 67:76-85.
11. Hillary R.M. Bayesian integrated survey-based assessments: an example applied to North Sea herring (Clupea harengus) survey data. Canadian Journal of Fisheries and Aquatic Sciences 2011, 68:1387-1407.
12. Hillary, R.M. Assessing components of uncertainty in VPA abundance and mortality estimates using an alternative exploitation rate-based algorithm. Natural Resources Modeling, in press. doi:10.1111/j.1939-7445.2011.00113.x.
13. Jeffreys H. Theory of Probability 1961, Oxford University Press, London.
14. Liermann M., Hilborn R. Depensation in fish stocks: a hierarchic Bayesian meta-analysis. Canadian Journal of Fisheries and Aquatic Sciences 1997, 54:1976-1984.
15. Mantzouni I., Sorensen H., O'Hara R.B., MacKenzie B.R. Hierarchical modelling of temperature and habitat size effects on population dynamics of North Atlantic cod. ICES Journal of Marine Science 2010, 67:833-855.
16. Meng X. Posterior predictive p-values. Annals of Statistics 1994, 22:1142-1160.
17. Michielsens C.G.J., McAllister M.K. A Bayesian hierarchical analysis of stock-recruit data: quantifying structural and parameter uncertainties. Canadian Journal of Fisheries and Aquatic Sciences 2004, 61:1032-1047.
18. Myers R.A. Stock and recruitment: generalizations about maximum reproductive rate, density dependence, and variability using meta-analytic approaches. ICES Journal of Marine Science 2001, 58:937-951.
19. Punt A.E., Smith D.C., Smith A.D.M. Among-stock comparisons for improving stock assessments of data-poor stocks: the "Robin Hood" approach. ICES Journal of Marine Science 2011, 68:972-981.
20. Quinn T.J., Deriso R.B. Quantitative Fish Dynamics 1999, Oxford University Press, New York.
21. Report of the herring assessment working group south of 62N (HAWG). ICES CM 2008/ACOM:02, Copenhagen, Denmark.
22. Spiegelhalter D.J., Best N.G., Carlin B.P., van der Linde A. Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society B 2002, 64:583-639.
23. Su Z., Peterman R.M., Haeseker S.L. Spatial hierarchical Bayesian models for stock-recruitment analysis of pink salmon. Canadian Journal of Fisheries and Aquatic Sciences 2004, 61:2471-2486.
24. Su Z., Peterman R.M. Performance of a Bayesian state-space model for semelparous species for stock-recruitment data subject to measurement error. Ecological Modelling 2012, 224:76-89.