Incorporating multiple sources of stochasticity into dynamic population models

Ecology

Volumn 84, Issue 6, 2003, Pages 1395-1402

  Calder, Catherine ^a   Lavine, Michael ^a   Müller, Peter ^a,b   Clark, James S ^a  

^a DUKE UNIVERSITY   (United States)

^b UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

Bayesian; Markov chain Monte Carlo (MCMC) simulation; Nonlinear models; Normal dynamic linear models; Observation error; Population dynamics; State space; Time series; Uncertainty

Indexed keywords

POPULATION DYNAMICS; POPULATION MODELING; STOCHASTICITY; TIME SERIES; UNCERTAINTY ANALYSIS;

EID: 0345570524     PISSN: 00129658     EISSN: None     Source Type: Journal    
DOI: 10.1890/0012-9658(2003)084[1395:IMSOSI]2.0.CO;2     Document Type: Review

References (27)

1. Bjørnstad, O. N., J.-M. Fromentin, N. C. Stenseth, and J. Gjøsæter. 1999. Cycles and trends in cod populations. Ecology 96:5066-5071.
2. Bjørnstad, O. N., and B. T. Grenfell. 2001. Noisy clockwork: time series analysis of population fluctuations in animals. Science 293:638-643.
3. Carlin, B. P., N. G. Polson, and D. S. Stoffer. 1992. A Monte Carlo approach to nonnormal and nonlinear state-space modeling. Journal of the American Statistical Association 87:493-500.
4. Carpenter, S. R., K. L. Cottingham, and C. A. Stow. 1994. Fitting predator-prey models to time series with observation errors. Ecology 75:1254-1264.
5. Carter, C. K., and R. Kohn. 1994. On Gibbs sampling for state space models. Biometrika 81(3):541-553.
6. Cottingham, K., and D. E. Schindler. 2000. Effects of grazer community structure on phytoplankton response to nutrient pulses. Ecology 81:183-200.
7. Coulson, T., E. A. Catchpole, S. D. Albon, B. J. T. Morgan, J. M. Pemberton, T. R. Clutton Brock, M. J. Crawley, and B. T. Grenfell. 2001. Age, sex, density, winter weather, and population crashes in soay sheep. Science 292:1528-1531.
8. Dennis, B., and M. L. Taper. 1994. Density dependence in time series observations of natural populations - estimation and testing. Ecological Monographs 64:205-224.
9. deValpine, P., and A. Hastings. 2002. Fitting population models with process noise and observation error. Ecological Monographs 72:57-76.
10. Frühwirth-Schnatter, S. 1994. Data augmentation and dynamic linear models. Journal of Time Series Analysis 15: 183-202.
11. Gelfand, A. E., and A. F. M. Smith. 1990. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85:129-133.
12. Gilks, W. R., S. Richardson, and D. J. Spiegelhalter. 1996. Markov chain Monte Carlo in practice. Chapman and Hall, New York, New York, USA.
13. Harrison, P. J., and D. F. Stevens. 1976. Bayesian forecasting (with discussion). Journal of the Royal Statistical Society, Series B 38:205-247.
14. Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, Series B 82:34-45.
15. Kass, R. E., and A. E. Raftery. 1995. Bayes factors. Journal of the American Statistical Association 90(430):773-795.
16. Meinhold, R. J., and N. D. Singpurwalla. 1983. Understanding the Kaiman filter. American Statistician 37(2):123-127.
17. Meyer, R., and R. B. Millar. 1999. BUGS in Bayesian stock assessment. Canadian Journal of Fisheries and Aquatic Sciences 56:1078-1086.
18. Millar, R. B., and R. Meyer. 2000a. State-space modeling of non-linear fisheries biomass dynamics using the Gibbs sampler. Applied Statistics 49:327-342.
19. Millar, R. B., and R. Meyer. 2000b. Bayesian state-space modeling of age structured data: fitting a model is just the beginning. Canadian Journal of Fisheries and Aquatic Sciences 57:43-50.
20. Newton, M. A., and A. E. Raftery. 1994. Approximate Bayesian inference by the weighted likelihood bootstrap (with discussion). Journal of the Royal Statistical Society, Series B 56:3-48.
21. Pole, A., M. West, and P. J. Harrison. 1994. Applied Bayesian forecasting and time series analysis. Chapman and Hall, New York, New York, USA.
22. Soudant, D., B. Beliaeff, and G. Thomas. 1997. Dynamic linear Bayesian models in phytoplankton ecology. Ecological Modelling 9:161-169.
23. Spiegelhalter, D. J., A. Thomas, N. Best, and W. R. Gilks. 1996. BUGS 0.5, Bayesian inference using Gibbs sampling. Manual version ii. Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, UK.
24. Stenseth, N. C. 1995. Snowshoe hare populations - squeezed from below and above. Science 269:1061-1062.
25. Stenseth, N. C., W. Falck, K. Chan, O. N. Bjørnstad, M. O'Donoghue, H. Tong, R. Boonstra, S. Boutin, C. Krebs, and N. G. Yoccoz. 1998. From patterns to processes: phase and density dependencies in the Canadian lynx cycle. Proceedings of the National Academy of Sciences (USA) 95: 15430-15435.
26. Tierney, L. 1994. Markov chains for exploring posterior distributions. Annals of Statistics 22:1701-1728.
27. West, M., and J. Harrison. 1997. Bayesian forecasting and dynamic models. Second edition. Springer-Verlag, New York, New York, USA.