Hidden process models for animal population dynamics

Ecological Applications

Volumn 16, Issue 1, 2006, Pages 74-86

  Newman, K B ^a   Buckland, S T ^a   Lindley, S T ^b   Thomas, L ^a   Fernandez C ^c  

^a UNIVERSITY OF ST ANDREWS   (United Kingdom)

^b SOUTHWEST FISHERIES SCIENCE CENTER   (United States)

^c LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

Endangered species; Kernel smoothing; Sequential importance sampling; State space models

Indexed keywords

ENDANGERED SPECIES; POPULATION DYNAMICS; SALMONID;

ANIMAL; BIODIVERSITY; BIOLOGICAL MODEL; CHEMISTRY; COMPUTER SIMULATION; CONFERENCE PAPER; DEMOGRAPHY; GROWTH, DEVELOPMENT AND AGING; MONTE CARLO METHOD; ONCORHYNCHUS; POPULATION DYNAMICS; PROBABILITY; RIVER; STATISTICAL MODEL;

ANIMALS; BIODIVERSITY; COMPUTER SIMULATION; DEMOGRAPHY; LIKELIHOOD FUNCTIONS; MARKOV CHAINS; MODELS, BIOLOGICAL; MONTE CARLO METHOD; ONCORHYNCHUS; POPULATION DYNAMICS; RIVERS;

CALIFORNIA; NORTH AMERICA; SACRAMENTO RIVER; UNITED STATES;

ANIMALIA; ONCORHYNCHUS; ONCORHYNCHUS TSHAWYTSCHA;

EID: 33645103546     PISSN: 10510761     EISSN: 10510761     Source Type: Journal    
DOI: 10.1890/04-0592     Document Type: Conference Paper

References (31)

1. Besbeas, P., S. N. Freeman, B. J. T. Morgan, and E. A. Catchpole. 2002. Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics 58:540-547.
2. Brooks, S. P., R. King, and B. J. T. Morgan. 2004. A Bayesian approach to combining animal abundance and demographic data. Animal Biodiversity and Conservation 27:515-529.
3. Buckland, S. T., K. B. Newman, L. Thomas, and N. B. Koesters. 2004. State-space models for the dynamics of wild animal populations. Ecological Modelling 171:157-175.
4. Catchpole, E. A., and B. J. T. Morgan. 1997. Detecting parameter redundancy. Biometrika 84:187-196.
5. Catchpole, E. A., and B. J. T. Morgan. 2001. Deficiency of parameter-redundant models. Biometrika 88:593-598.
6. Doucet, A., N. Freitas, and N. Gordon. 2001. Sequential Monte Carlo methods in practice. Springer-Verlag, New York, New York, USA.
7. Geyer, C. 1996. Estimation and optimization of functions. Pages 241-258 in W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, editors. Markov chain Monte Carlo in practice. Chapman and Hall, New York, New York, USA.
8. Gilks, W. R., S. Richardson, and D. J. Spiegelhalter. 1996. Markov chain Monte Carlo in practice. Chapman and Hall, New York, New York, USA.
9. Godsill, S. J., A. Doucet, and M. West. 2004. Monte Carlo smoothing for nonlinear time series. Journal of the American Statistical Association 99:156-168.
10. Gotelli, N. 2001. A primer of ecology. Third edition. Sinauer Associates, Sunderland, Massachusetts, USA.
11. Gurney, W. S. C., and R. M. Nisbet. 1998. Ecological dynamics. Oxford University Press, Oxford, UK.
12. Hallock, R. J., and F. W. Fisher. 1985. Status of winter-run chinook salmon, Oncorhynchus tshawytscha, in the Sacramento River. Anadromous Fisheries Branch Office Report. California Department of Fish and Game, Sacramento, California, USA.
13. Hilborn, R., and M. Mangel. 1997. The ecological detective. Princeton University Press, Princeton, New Jersey, USA.
14. Hilborn, R., and C. J. Walters. 1992. Quantitative fisheries stock assessment: choice, dynamics, and uncertainty. Chapman and Hall, New York, New York, USA.
15. Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. Transactions of ASME Journal of Basic Engineering 82:35-45.
16. Lindley, S. T. 2003. Estimation of population growth and extinction parameters from noisy data. Ecological Applications 13:806-813.
17. Liu, J. S. 2001. Monte Carlo strategies in scientific computing. Springer-Verlag, Berlin, Germany.
18. Liu, J. S., and R. Chen. 1998. Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association 93:1032-1044.
19. Liu, J., and M. West. 2001. Combined parameter and state estimation in simulation-based filtering. Pages 197-223 in A. Doucet, N. de Freitas, and N. Gordon, editors. Sequential Monte Carlo methods in practice. Springer-Verlag, Berlin, Germany.
20. Martin, C. D., P. D. Gaines, and R. R. Johnson. 2001. Estimating the abundance of Sacramento River juvenile winter chinook salmon with comparisons to adult escapement. Final Report, Red Bluff Research Pumping Plant Report Series Volume 5. U.S. Fish and Wildlife Office, Red Bluff, California, USA.
21. Mendelssohn, R. 1988. Some problems in estimating sizes from catch-at-age data. Fishery Bulletin 86:617-630.
22. Meyer, R., and R. B. Millar. 1999. Bayesian stock assessment using a state-space implementation of the delay difference model. Canadian Journal of Fisheries and Aquatic Sciences 56:37-52.
23. Newman, K. B. 1998. State-space modeling of animal movement and mortality with application to salmon. Biometrics 54:1290-1314.
24. Pitt, M. K., and N. Shephard. 1999. Filtering via simulation: auxiliary particle filters. Journal of the American Statistical Association 94:590-599.
25. Quinn, T. J., and R. B. Deriso. 1999. Quantitative fish dynamics. Oxford University Press, Oxford, UK.
26. Schnute, J. 1994. A general framework for developing sequential fisheries models. Canadian Journal of Fisheries and Aquatic Sciences 51:1676-1688.
27. Speed, T. 1993. Modelling and managing a salmon population. Pages 267-292 in V. Barnett and K. F. Turkman, editors. Statistics for the environment. John Wiley and Sons, New York, New York, USA.
28. Sullivan, P. 1992. A Kalman filter approach to catch-atlength-analysis. Biometrics 48:237-257.
29. Thomas, L., S. T. Buckland, K. B. Newman, and J. Harwood. 2005. A unified framework for modelling wildlife population dynamics. Australian and New Zealand Journal of Statistics 47(1):19-34.
30. Trenkel, V. M., D. A. Elston, and S. T. Buckland. 2000. Calibrating population dynamics models to count and cull data using sequential importance sampling. Journal of American Statistical Association 95:363-374.
31. West, M., and J. Harrison. 1989. Bayesian forecasting and dynamic models. Springer-Verlag, New York, New York, USA.