Dealing with missing covariate data in fishery stock assessment models

Fisheries Research

Volumn 101, Issue 1-2, 2010, Pages 80-86

  Maunder, Mark N ^a,c   Deriso, Richard B ^b,c  

^a Quantitative Resource Assessment LLC   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c INTER AMERICAN TROPICAL TUNA COMMISSION   (United States)

Author keywords

Covariate; Environmental variable; Fisheries; Missing data; Random effect; Recruitment; Stock assessment

Indexed keywords

EID: 70449628905     PISSN: 01657836     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fishres.2009.09.009     Document Type: Article

References (30)

1. Bull B., and Livingston M.E. Links between climate variation and year class strength of New Zealand hoki (Macruronus novaezelandiae): an update. N. Z. J. Mar. Freshw. Res. 35 (2001) 871-880
2. Cury P., and Roy C. Optimal environmental window and pelagic fish recruitment success in upwelling areas. Can. J. Fish. Aquat. Sci. 46 (1989) 670-680
3. Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B (Met.) 39 (1977) 1-38
4. Deriso R.B., Maunder M.N., and Pearson W.H. Incorporating covariates into fisheries stock assessment models with application to Pacific herring of Prince William Sound, Alaska. Ecol. Appl. 18 (2008) 1270-1286
5. Deriso R.B., Maunder M.N., and Skalski J.R. Variance estimation in integrated assessment models and its importance for hypothesis testing. Can. J. Fish. Aquat. Sci. 64 (2007) 187-197
6. Francis M.P. Does water temperature determine year class strength in New Zealand snapper (Pagrus auratus, Sparidae). Fish. Ocean. 2 (1993) 65-72
7. Gimenez, O., Bonner, S., King, R., Parker, R.A., Brooks, S., Jamieson, L.E., Grosbois, V., Morgan, B.J.T., Thomas, L., 2009. WinBUGS for population ecologists: Bayesian modeling using Markov Chain Monte Carlo methods. In: Thomson, D.L., Cooch, E.G., Conroy, M.J. (Eds.), Modeling Demographic Processes in Marked Populations. Env. Ecol. Stat., vol. 3, pp. 883-916.
8. Horton N.J., and Laird N.M. Maximum likelihood analysis of generalized linear models with missing covariates. Stat. Meth. Med. Res. 8 (1998) 37-50
9. Lewy P., and Nielsen A. Modeling stochastic fish stock dynamics using Markov Chain Monte Carlo. ICES J. Mar. Sci. 60 (2003) 743-752
10. Little R.J.A. Regression with missing X's: a review. J. Am. Stat. Assoc. 87 (1992) 1227-1237
11. Little R.J.A., and Rubin D.B. Statistical Analysis with Missing Data (1987), John Wiley, New York
12. Marty G.D., Quinn II T.J., Carpenter G., Meyers T.R., and Willitis N.H. Role of disease in abundance of a Pacific herring (Clupea pallasi) population. Can. J. Fish. Aquat. Sci. 60 (2003) 1258-1265
13. Maunder, M.N., 1998. Problems with using an environmental based recruitment index: examples from a New Zealand snapper (Pagrus auratus) assessment. In: Funk, F., Quinn II, T.J., Heifetz, J., Ianelli, J.N., Powers, J.E., Schweigert, J.J., Sullivan, P.J., Zhang, C.I. (Eds.), Fishery Stock Assessment Models. Alaska Sea Grant College Program Report No. AK-SG-98-01, University of Alaska Fairbanks, pp. 679-692.
14. Maunder M.N. A general framework for integrating the standardization of catch-per-unit-of-effort into stock assessment models. Can. J. Fish. Aquat. Sci. 58 (2001) 795-803
15. Maunder M.N., and Deriso R.B. Estimation of recruitment in catch-at-age models. Can. J. Fish. Aquat. Sci. 60 (2003) 1204-1216
16. Maunder M.N., and Punt A.E. Standardizing catch and effort data: a review of recent approaches. Fish. Res. 70 (2004) 141-159
17. Maunder M.N., Sibert J.R., Fonteneau A., Hampton J., Kleiber P., and Harley S.J. Interpreting catch-per-unit-of-effort data to asses the status of individual stocks and communities. ICES J. Mar. Sci. 63 (2006) 1373-1385
18. Maunder M.N., and Watters G.M. A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example. Fish. Bull. 101 (2003) 89-99
19. Methot, R.D., 2009. User Manual for Stock Synthesis, Model Version 3.03a. Unpublished manuscript, available from NOAA Fisheries Stock Assessment Toolbox website: http://nft.nefsc.noaa.gov/, p. 143.
20. Mertz G., and Myers R.A. Estimating the predictability of recruitment. Fish. Bull. 93 (1995) 657-665
21. Millar R.B. Simulated maximum likelihood applied to non-Gaussian and nonlinear mixed effects and state-space models. Aust. N. Z. J. Stat. 46 (2004) 515-526
22. Myers R.A. When do environment-recruit correlations work?. Rev. Fish Biol. Fish. 8 (1998) 285-305
23. Rubin B.D. Inference and missing data. Biometrika 63 (1976) 581-592
24. Schafer J.L., and Graham J.W. Missing data: our view of the state of the art. Psychol. Methods 7 (2002) 147-177
25. Sibert J., Hampton J., Kleiber P., and Maunder M.N. Biomass, size, and trophic status of top predators in the Pacific Ocean. Science 314 (2006) 1773-1776
26. Skaug H.J., and Fournier D.A. Automatic approximation of h marginal likelihood in non-Gaussian hierarchical models. Comp. Stat. Data Anal. 51 (2006) 699-709
27. Spiegelhalter, D., Thomas, A., Best, N., Lunn, D., 2004. WinBUGS User Manual, version 2.0 (http://mathstat.helsinki.fi/openbugs/).
28. Walters C.J., and Collie J.S. Is research on environmental factors useful to fisheries management?. Can. J. Fish. Aquat. Sci. 45 (1988) 1848-1854
29. Watters G.M., and Maunder M.N. Status of bigeye tuna in the eastern Pacific Ocean. Inter-Am. Trop. Tuna Comm. Stock Ass. Rep. 2 (2002) 147-246
30. Wang S.-.P., Maunder M.N., Aires-da-Silva A., and Bayliff W.H. Evaluating fishery impacts: application to bigeye tuna (Thunnus obesus) in the eastern Pacific Ocean. Fish. Res. 99 (2009) 106-111