Assessing components of uncertainty in VPA abundance and mortality estimates using an alternative exploitation rate-based algorithm

Natural Resource Modeling

Volumn 25, Issue 4, 2012, Pages 574-598

  Hillary, R M ^a  

^a CSIRO   (Australia)

Author keywords

Catch at age; Exploitation rate; Uncertainty; Virtual population analysis

Indexed keywords

APPROXIMATION ALGORITHMS; POPULATION STATISTICS;

ANALYSIS ALGORITHMS; CATCH-AT-AGE; EXPLOITATION RATE; GENERALISATION; POPULATION ANALYSIS; RATE BASED ALGORITHM; UNCERTAINTY; VARIANCE ESTIMATE; VARIANCE INFORMATION; VIRTUAL POPULATION ANALYSE;

UNCERTAINTY ANALYSIS;

EID: 84868497160     PISSN: 08908575     EISSN: 19397445     Source Type: Journal    
DOI: 10.1111/j.1939-7445.2011.00113.x     Document Type: Article

References (31)

1. M. Aksland 1994], A general cohort analysis method, Biometrics. 50(4), 917-932.
2. F.I. Baranov 1918], On the question of the biological basis of fisheries, Nauch. Issledov. Iktiol. Inst. Izv. I (1), Moscow, pp. 81-128.
3. T.A. Branch 2009], Differences in predicted catch composition between two widely used catch equation formulations, Can. J. Fish. Aquat. Sci. 66, 126-132.
4. M.J. Bradford 1991], Effects of ageing errors on recruitment time series estimated from sequential population analysis, Can. J. Fish. Aquat. Sci. 48, 555-558.
5. B. Bull, R.I.C.C. Francis, A. Dunn, A. McKenzie, D.J. Gilbert and M.H. Smith 2005], CASAL User Manual v2.07-2005/07/06, NIWA Technical Report, 126.
6. D.A. Fournier, J. Hampton, and J.R. Sibert 1998], MULTIFAN-CL: a length-based, age-structured model for fisheries stock assessment, with application to South Pacific albacore, Thunnus alalunga, Can. J. Fish. Aquat. Sci. 55, 2105-2116.
7. S. Gavaris 1988], An adaptive framework for the estimation of population size, Canadian Atlantic Fisheries Science Advisory Committee (CAFSAC), Dartmouth, N.S. Res. Doc. 88/29.
8. J. Horwood 1982], The variance of population and yield from an age-structured stock, with application to the North Sea herring (Clupea harengus), ICES J. Mar. Sci. 40, 237-244.
9. J. Horwood 1983], A General Linear Theory for the Variance of the Yield from Fish Stocks, Mathematical Biosciences. 64, 203-225.
10. ICES-HAWG [2008], Report of the Herring Assessment Working Group South of 62N (HAWG). ICES CM 2008/ACOM:02, Copenhagen, Denmark.
11. ICES-WGMG [2009], Report of the Working Group on Methods of Fish Stock Assessment (WGMG), ICES CM 2009/RMC:12, Copenhagen, Denmark.
12. L.T. Kell, I. Mosqueira, P. Grosjean, J.-M. Fromentin, D. Garcia, R.M. Hillary, E. Jardim, S. Mardle, M.A. Pastoors, J.-J. Poos, F. Scott, and R.D. Scott 2007], FLR: an open source framework for the evaluation and development of management strategies, ICES J. Mar. Sci. 64, 640-646.
13. A.D. MacCall 1986], Virtual population analysis (VPA) equations for nonhomogeneous populations, and a family of approximations including improvements on Pope's cohort analysis, Can. J. Fish. Aquat. Sci. 43, 2406-2409.
14. M. Maunder, and R.B. Deriso 2003], Estimation of recruitment in catch-at-age models, Can. J. Fish. Aquat. Sci. 60, 1204-1216.
15. M.K. McAllister, and J.N. Ianelli 1997], Bayesian stock assessment using catch-age data and the sampling-importance resampling algorithm, Can. J. Fish. Aquat. Sci. 54, 284-300.
16. G. Mertz, and R.A. Myers 1996], An extended cohort analysis: incorportating the effect of seasonal catches, Can. J. Fish. Aquat. Sci. 53, 159-163.
17. R.D. Methot 1989], Synthetic estimates of historical abundance and mortality for northern anchovy, Am. Fish. Soc. Symp. 6, 66-82.
18. R.B. Miller, and R. Meyer 2000], Bayesian state-space modeling of age-structured data: fitting a model is just the beginning, Can. J. Fish. Aquat. Sci. 57, 43-50.
19. G.W. Oehlert 1992], A Note on the Delta Method. The American Statistician, 46(1), 27-29.
20. K. Patterson, and G. Melvin 1996], Integrated catch-at-age analysis version 1.2, Stochttish Fisheries Research Report, 56. FRS Marine Laboratory, Aberdeen, UK.
21. K.R. Patterson 1999], Evaluating uncertainty in harvest control law catches using Bayesian Markov chain Monte Carlo virtual population analysis with adaptive rejection sampling and including structural uncertainty, Can. J. Fish. Aquat. Sci. 56, 208-221.
22. J.G. Pope 1972], An investigation of the accuracy of virtual population analysis using cohort analysis, Int. Comm. Northwest Atl. Fish. Res. Bull. 9, 65-74.
23. C.E. Porch 2002], VPA-2BOX version 3.0 user's guide. NOAA Sustainable Fisheries Division Contribution SFD-01/02-151.
24. A.E. Punt, and R. Hilborn 1997], Fisheries stock assessment and decision analysis: the Bayesian approach, Rev. Fish. Bio. Fish. 7, 35-63.
25. T.J. Quinn II, and R.B. Deriso 1999], Quantitative Fish Dynamics, Oxford University Press, New York.
26. S.A. Reeves 2003], A simulation study of the implication of age-reading errors for stock assessment and management advice, ICES J. Mar. Sci. 60, 314-328.
27. D.B. Sampson 1987], Variance estimators for Virtual Population Analysis, J. Cons. Int. Explor. Mer 43, 149-158.
28. J.G. Shepherd 1999], Extended Survivors Analysis: an improved method for the analysis of catch-at-age data and abundance indices, ICES J. Mar. Sci. 56, 584-591.
29. S.E. Sims 1982], Algorithms for solving the catch equation forward and backward in time, Can. J. Fish. Aquat. Sci. 39, 197-202.
30. Ø. Ulltang 1977], Sources of errors in and limiations of virtual population analysis (cohort analysis), J. Cons. Int. Explor. Mer. 37, 249-260.
31. G. Wang 2007], On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models, Ecological Modelling, 200(3-4), 521-528.