ASSESSING COMPONENTS OF UNCERTAINTY IN VPA ABUNDANCE AND MORTALITY ESTIMATES USING AN ALTERNATIVE EXPLOITATION RATE-BASED ALGORITHM

Using an exploitation rate (not fishing mortality) based virtual population analysis (VPA) algorithm, which is itself a generalization of the Pope approximation to the original VPA equations, I show how to derive variance estimates for the key VPA outputs (recruitment, SSB, exploitation rates) given...

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Published inNatural resource modeling Vol. 25; no. 4; pp. 574 - 598
Main Author HILLARY, R. M.
Format Journal Article
LanguageEnglish
Published Malden, USA Blackwell Publishing Inc 01.11.2012
John Wiley & Sons, Inc
Subjects
Online AccessGet full text
ISSN0890-8575
1939-7445
DOI10.1111/j.1939-7445.2011.00113.x

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Abstract Using an exploitation rate (not fishing mortality) based virtual population analysis (VPA) algorithm, which is itself a generalization of the Pope approximation to the original VPA equations, I show how to derive variance estimates for the key VPA outputs (recruitment, SSB, exploitation rates) given variance information on the key inputs (catch‐at‐age/terminal exploitation rates). Given the alternative VPA algorithm permits closed‐form solutions for the outputs in terms of the inputs, the delta method is employed to obtain the variance estimates, removing the need for complex simulation techniques. Using North Sea herring data as an example, the method’s utility is demonstrated by exploring the impact of aging error in the catch data and tuning error on the precision of estimates of SSB, recruitment and exploitation rates, and the parameters of the stock‐recruit relationship.
AbstractList Abstract Using an exploitation rate (not fishing mortality) based virtual population analysis (VPA) algorithm, which is itself a generalization of the Pope approximation to the original VPA equations, I show how to derive variance estimates for the key VPA outputs (recruitment, SSB, exploitation rates) given variance information on the key inputs (catch-at-age/terminal exploitation rates). Given the alternative VPA algorithm permits closed-form solutions for the outputs in terms of the inputs, the delta method is employed to obtain the variance estimates, removing the need for complex simulation techniques. Using North Sea herring data as an example, the method's utility is demonstrated by exploring the impact of aging error in the catch data and tuning error on the precision of estimates of SSB, recruitment and exploitation rates, and the parameters of the stock-recruit relationship. [PUBLICATION ABSTRACT]
Using an exploitation rate (not fishing mortality) based virtual population analysis (VPA) algorithm, which is itself a generalization of the Pope approximation to the original VPA equations, I show how to derive variance estimates for the key VPA outputs (recruitment, SSB, exploitation rates) given variance information on the key inputs (catch‐at‐age/terminal exploitation rates). Given the alternative VPA algorithm permits closed‐form solutions for the outputs in terms of the inputs, the delta method is employed to obtain the variance estimates, removing the need for complex simulation techniques. Using North Sea herring data as an example, the method’s utility is demonstrated by exploring the impact of aging error in the catch data and tuning error on the precision of estimates of SSB, recruitment and exploitation rates, and the parameters of the stock‐recruit relationship.
Abstract Using an exploitation rate (not fishing mortality) based virtual population analysis (VPA) algorithm, which is itself a generalization of the Pope approximation to the original VPA equations, I show how to derive variance estimates for the key VPA outputs (recruitment, SSB, exploitation rates) given variance information on the key inputs (catch‐at‐age/terminal exploitation rates). Given the alternative VPA algorithm permits closed‐form solutions for the outputs in terms of the inputs, the delta method is employed to obtain the variance estimates, removing the need for complex simulation techniques. Using North Sea herring data as an example, the method’s utility is demonstrated by exploring the impact of aging error in the catch data and tuning error on the precision of estimates of SSB, recruitment and exploitation rates, and the parameters of the stock‐recruit relationship.
Author HILLARY, R. M.
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  email: CSIRO Marine and Atmospheric Research, Wealth from Oceans National Research Flagship, Castray Esplanade, Hobart, TAS 7001, Australia  rich.hillary@csiro.au, rich.hillary@csiro.au
  organization: CSIRO Marine and Atmospheric Research, Wealth from Oceans National Research Flagship, Castray Esplanade, Hobart, TAS 7001, Australia E-mail: rich.hillary@csiro.au
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– reference: 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.
– reference: 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.
– reference: G.W. Oehlert 1992], A Note on the Delta Method. The American Statistician , 46(1), 27-29.
– reference: ICES-WGMG [2009], Report of the Working Group on Methods of Fish Stock Assessment (WGMG) , ICES CM 2009/RMC:12, Copenhagen , Denmark .
– reference: R.D. Methot 1989], Synthetic estimates of historical abundance and mortality for northern anchovy , Am. Fish. Soc. Symp. 6, 66-82.
– reference: 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.
– reference: A.E. Punt, and R. Hilborn 1997], Fisheries stock assessment and decision analysis: the Bayesian approach , Rev. Fish. Bio. Fish. 7, 35-63.
– reference: F.I. Baranov 1918], On the question of the biological basis of fisheries , Nauch. Issledov. Iktiol. Inst. Izv. I (1), Moscow , pp. 81-128.
– reference: 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.
– reference: J. Horwood 1983], A General Linear Theory for the Variance of the Yield from Fish Stocks , Mathematical Biosciences. 64, 203-225.
– reference: 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.
– reference: 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.
– reference: 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.
– reference: 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.
– reference: S.E. Sims 1982], Algorithms for solving the catch equation forward and backward in time , Can. J. Fish. Aquat. Sci. 39, 197-202.
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– reference: M. Aksland 1994], A general cohort analysis method , Biometrics. 50(4), 917-932.
– reference: D.B. Sampson 1987], Variance estimators for Virtual Population Analysis , J. Cons. Int. Explor. Mer 43, 149-158.
– reference: M. Maunder, and R.B. Deriso 2003], Estimation of recruitment in catch-at-age models , Can. J. Fish. Aquat. Sci. 60, 1204-1216.
– reference: T.J. Quinn II, and R.B. Deriso 1999], Quantitative Fish Dynamics , Oxford University Press, New York .
– reference: T.A. Branch 2009], Differences in predicted catch composition between two widely used catch equation formulations , Can. J. Fish. Aquat. Sci. 66, 126-132.
– reference: 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.
– reference: K. Patterson, and G. Melvin 1996], Integrated catch-at-age analysis version 1.2 , Stochttish Fisheries Research Report, 56. FRS Marine Laboratory, Aberdeen , UK .
– reference: 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.
– reference: 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.
– reference: 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.
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Snippet Using an exploitation rate (not fishing mortality) based virtual population analysis (VPA) algorithm, which is itself a generalization of the Pope...
Abstract Using an exploitation rate (not fishing mortality) based virtual population analysis (VPA) algorithm, which is itself a generalization of the Pope...
Abstract Using an exploitation rate (not fishing mortality) based virtual population analysis (VPA) algorithm, which is itself a generalization of the Pope...
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SubjectTerms Algorithms
catch-at-age
Estimates
exploitation rate
Marine
Studies
uncertainty
Virtual population analysis
Title ASSESSING COMPONENTS OF UNCERTAINTY IN VPA ABUNDANCE AND MORTALITY ESTIMATES USING AN ALTERNATIVE EXPLOITATION RATE-BASED ALGORITHM
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