A state-space mixture approach for estimating catastrophic events in time series data

Canadian Journal of Fisheries and Aquatic Sciences

Volumn 64, Issue 6, 2007, Pages 899-910

  Ward, Eric J ^a   Hilborn, Ray ^a   Towell, Rod G ^b   Gerber, Leah ^c  

^a UNIVERSITY OF WASHINGTON   (United States)

^b NOAA National Marine Mammal Laboratory   (United States)

^c ARIZONA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ABUNDANCE; CATASTROPHIC EVENT; ERROR ANALYSIS; ESTIMATION METHOD; EXTINCTION RISK; PINNIPED; POPULATION MODELING; TIME SERIES;

CALLORHINUS; CALLORHINUS URSINUS;

EID: 34548157386     PISSN: 0706652X     EISSN: None     Source Type: Journal    
DOI: 10.1139/F07-060     Document Type: Article

References (39)

1. Akaike, H. 1973. Information theory as an extension of the maximum likelihood principle. In 2nd International Symposium on Information Theory, Tsahkadsor, Armenia, USSR, 2-8 September 1971. Edited by B.N. Petrov and F. Csaksi. Akademiai Kiado, Budapest, Hungary. pp. 267-281.
2. Angliss, R.P., and Lodge, K.L. 2002. Alaska marine mammal stock assessments, 2002. US Department of Commerce, Seattle, Wash. NOAA Tech. Memo. NMFS-AFSC-133.
3. Calder, C., Lavine, M., Muller, P., and Clark, J.S. 2003. Incorporating multiple sources of stochasticity into population dynamics models. Ecology, 84: 1395-1402.
4. Carlin, B.P., and Louis, T.A. 2000. Bayes and empirical Bayes methods for data analysis. Chapman and Hall, New York.
5. Chen, Y., and Fournier, D. 1999. Impacts of atypical data on Bayesian inference and robust Bayesian approach in fisheries. Can. J. Fish. Aquat. Sci. 56: 1525-1533.
6. Clark, J.S., and Bjørnstad, O.N. 2004. Population time series: process variability, observation errors, missing values, lags, and hidden states. Ecology, 85: 3140-3150.
7. de Valpine, P. 2002. Review of methods for fitting time-series models with process and observation error and likelihood calculations for nonlinear, non-gaussian state-space models. Bull. Mar. Sci. 70: 455-471.
8. de Valpine, P., and Hastings, A. 2002. Fitting population models incorporating process noise and observation error. Ecol. Monogr. 72: 57-76.
9. Erb, J.D., and Boyce, M. 1999. Distribution of population declines in large mammals. Conserv. Biol. 13: 199-201.
10. Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. 1995. Bayesian data analysis. Chapman and Hall, New York.
11. Gerber, L.R., and Hilborn, R. 2001. Catastrophic events and recovery from low densities in populations of otariids: implications for risk of extinction. Mammal Rev. 31: 131-150.
12. Gilks, W.R., Richardson, S., and Spiegelhalter, D. 1996. Markov Chain Monte Carlo in practice. Chapman and Hall, New York.
13. Gilpin, M.E., and Ayala, F.J. 1973. Global models of growth and competition. PNAS, 70: 3950-3593.
14. Hilborn, R., and Mangel, M. 1997. The ecological detective: confronting models with data. Princeton University Press, Princeton, N.J.
15. Hobbs, N.T., and Hilborn, R. 2006. Alternatives to statistical hypothesis testing in ecology: a guide to self teaching. Ecol. Appl. 16: 5-19.
16. Holmes, E.E., and Fagan, W.E. 2002. Validating population viability analysis for corrupted data sets. Ecology, 83: 2379-2386.
17. Kalman, R.E. 1960. A new approach to linear filtering and prediction problems. Journal of Basic Engineering (Transactions of the ASME), 82: 35-45.
18. Lande, R., Steinar, E., and Saether, B.E. 2003. Stochastic population dynamics in ecology and conservation. Oxford University Press, New York.
19. Loughlin, T.R., Antonelis, G.A., Baker, J.D., York, A.E., Fowler, C.W., DeLong, R.L., and Braham, H.W. 1994. Status of the northern fur seal population in the United States during 1992. In Fur seal investigations, 1992. Edited by E.H. Sinclair. US Department of Commerce, Seattle, Wash. NOAA Tech. Memo. NMFS-AFSC-45.
20. Mangel, M., and Tier, C. 1994. Four facts every conservation biologist should know about persistence. Ecology, 75: 607-614.
21. McAllister, M.K., and Kirkwood, G.P. 1998. Bayesian stock assessment: a review and example application using the logistic model. ICES J. Mar. Sci. 55: 1031-1060.
22. Meyer, R., and Millar, R.B. 1999a. BUGS in Bayesian stock assessments. Can. J. Fish. Aquat. Sci. 56: 1078-1087.
23. Meyer, R., and Millar, R.B. 1999b. Bayesian stock assessment using a state-space implementation of the delay difference model. Can. J. Fish. Aquat. Sci. 56: 37-52.
24. NERC. 1999. The global population dynamics database [online]. NERC Center for Population Biology, Imperial College London, Ascot, Berkshire, UK. Available from www.sw.ic.ac.uk/cpb/gpdd.html [accessed 1 June 2006; updated September 2005].
25. Punt, A.E. 2003. Extending production models to deal with process error in the population dynamics. Can. J. Fish. Aquat. Sci. 60: 1217-1228.
26. Punt, A.E., and Hilborn, R. 1997. Fisheries stock assessment and decision analysis: the Bayesian approach. Rev. Fish Biol. Fish. 7(1): 35-63.
27. Rivot, E., Prevost, E., Parent, E., and Bagliniere, J.L. 2004. A Bayesian state-space modelling framework for fitting a salmon stage-structured population dynamic model to multiple time series of field data. Ecol. Model. 179: 463-485.
28. Seber, G.A.F. 1982. The estimation of animal abundance and related parameters. Griffin, London.
29. Spiegelhalter, D., Thomas, A., and Best, N. 2003. WinBUGS version 1.4 user manual [online]. Available from www.mrc-bsu.cam.ac.uk/bugs [accessed 01 June 2006].
30. Staples, D.F., Taper, M.L., and Dennis, B. 2004. Estimating population trend and process variation for PVA in the presence of sampling error. Ecology, 85: 923-929.
31. Taylor, B.L., and Gerrodette, T. 1993. The uses of statistical power in conservation biology: the Vaquita and Northern Spotted Owl. Conserv. Biol. 7: 489-500.
32. Thomas, L., Buckland, S.T., Newman, K.B., and Harwood, J. 2005. A unified framework for modelling wildlife population dynamics. Aust. N.Z. J. Stat. 47: 19-34.
33. Towell, R.G., Ream, R.R., and York, A.E. 2006. Decline in northern fur seal (Callorhinus ursinus) pup production on the Pribilof Islands. Mar. Mamm. Sci. 22: 486-491.
34. Walters, C.J., and Ludwig, D. 1981. Effects of measurement errors on the assessment of stock-recruitment relationship. Can. J. Fish. Aquat. Sci. 38: 704-710.
35. Walters, C.J., and Ludwig, D. 1994. Calculation of Bayes posterior probability distributions for key population parameters. Can. J. Fish. Aquat. Sci. 51: 713-722.
36. York, A.E. 1991. Sea surface temperatures and their relationship to the survival of juvenile male northern fur seals from the Pribilof Islands. In Pinnipeds and El Niño. Edited by F. Trillmich and K.A. Ono. Springer-Verlag, Berlin, Germany. pp. 94-106.
37. York, A.E., Towell, R.G., Ream, R.R., Baker, J.D., and Robson, B.W. 2000. Population assessment, Pribilof Islands, Alaska. In Fur seal investigations, 1998. Edited by B.W. Robson. US Department of Commerce, Seattle, Wash. NOAA Tech. Memo. NMFS-AFSC-113. pp. 7-26.
38. York, A.E., Towell, R.G., Ream, R.R., and Fowler, C.W. 2002. Population assessment, Pribilof Islands, Alaska. In Fur seal investigations, 2000-2001. Edited by B.W. Robson. US Department of Commerce, Seattle, Wash. NOAA Tech. Memo. NMFS-AFSC-134. pp. 7-32.
39. Young, T.P. 1994. Natural die-offs of large mammals: implications for conservation. Conserv. Biol. 8: 410-418.