Hierarchical Bayesian models for predicting the spread of ecological processes

Ecology

Volumn 84, Issue 6, 2003, Pages 1382-1394

  Wikle, Christopher K ^a  

^a UNIVERSITY OF MISSOURI   (United States)

Author keywords

Bayesian models; Carpodacus mexicanus; Diffusion; Forecast; Hierarchical models; House Finch; Invasive species; State space; Uncertainty

Indexed keywords

BAYESIAN ANALYSIS; ECOLOGICAL MODELING; PREDICTION; UNCERTAINTY ANALYSIS;

CARPODACUS MEXICANUS; FRINGILLIDAE;

EID: 0041985115     PISSN: 00129658     EISSN: None     Source Type: Journal    
DOI: 10.1890/0012-9658(2003)084[1382:HBMFPT]2.0.CO;2     Document Type: Review

References (29)

1. Berliner, L. M. 1996. Hierarchical Bayesian time series models. Pages 15-22 in K. Hanson and R. Silver, editors. Maximum entropy and Bayesian methods. Kluwer Academic Publishers, Dordrecht, The Netherlands.
2. Berliner, L. M., C. K. Wikle, and N. Cressie. 2000. Long-lead prediction of Pacific SSTs via Bayesian dynamic modeling. Journal of Climate 13:3953-3968.
3. Clark, J. S., et al. 2001. Ecological forecasts: an emerging imperative. Science 293:657-660.
4. Diggle, P. J., J. A. Tawn, and R. A. Moyeed. 1998. Model-based geostatistics (with discussion). Applied Statistics 47: 299-350.
5. Elliott, J. J., and R. S. Arbib. 1953. Origin and status of the house finch in the eastern United States. Auk 70:31-37.
6. Gilks, W. R., S. Richardson, and D. J. Spiegelhalter, editors. 1996. Markov chain Monte Carlo in practice. Chapman and Hall, London, UK.
7. Hastings, A. 1996. Models of spatial spread: is the theory complete? Ecology 77:1675-1679.
8. Lele, S., M. L. Taper, and S. Gage. 1998. Statistical analysis of population dynamics in space and time using estimating functions. Ecology 79:1489-1502.
9. Levin, S. A., B. Grenfall, A. Hastings, and A. S. Perelson. 1997. Mathematical and computational challenges in population biology and ecosystems science. Science 275:334-343.
10. Link, W. A., and J. R. Sauer. 1998. Estimating population change from count data: application to the North American Breeding Bird Survey. Ecological Applications 8:258-268.
11. McCulloch, C. E., and S. R. Searle. 2001. Generalized, linear, and mixed models. John Wiley and Sons, New York, New York, USA.
12. Mundinger, P. C., and S. Hope. 1982. Expansion of the winter range of the House Finch: 1947-79. American Birds 36: 347-353.
13. Okubo, A. 1986. Diffusion-type models for avian range expansion. Pages 1038-1049 in Henri Ouellet, editor. Acta XIX Congressus Internationalis Ornithologici. National Museum of Natural Sciences, University of Ottawa Press, Ottawa, Ontario, Canada.
14. Pfeifer, P. E., and S. J. Deutsch. 1980. Identification and interpretation of first order space-time ARMA models. Technometrics 22:397-408.
15. Robbins, C. S., D. A. Bystrak, and P. R. Geissler. 1986. The Breeding Bird Survey: its first fifteen years, 1965-1979. Fish and Wildlife Service Resource Publication 157. U.S. Department of Interior, Washington, D.C., USA.
16. Robert, C. P., and G. Casella. 1999. Monte Carlo statistical methods. Springer-Verlag, New York, New York, USA.
17. Royle, J. A., W. A. Link, and J. R. Sauer. 2002. Statistical mapping of count survey data. Pages 625-638 in J. M. Scott, P. J. Heglund, M. Morrison, M. Raphael, J. Haufler, and B. Wall, editors. Predicting species occurrences: issues of scale and accuracy. Island, Covello, California, USA.
18. Sauer, J. R., G. W. Pendleton, and S. Orsillo. 1995. Mapping of bird distributions from point-count surveys. Pages 151-160 in C. J. Ralph, J. R. Sauer, and S. Droege, editors. Monitoring bird populations by point counts. USDA Forest Service, Pacific Southwest Research Station, General Technical Report PSW GTR-149.
19. Sauer, J. R., B. G. Peterjohn, and W. A. Link. 1994. Observer differences in the North American Breeding Bird Survey. Auk 111:50-62.
20. Shigesada, N., K. Kawasaki, and E. Teramoto. 1986. Traveling periodic waves in heterogeneous environments. Theoretical Population Biology 30:143-160.
21. Skellam, J. G. 1951. Random dispersal in theoretical populations. Biometrika 38:196-218.
22. Veit, R. R., and M. A. Lewis. 1996. Dispersal, population growth, and the Allee effect: dynamics of the House Finch invasion of eastern North America. American Naturalist 148:255-274.
23. West, M., and J. Harrison. 1997. Bayesian forecasting and dynamic models. Springer-Verlag: New York, New York, USA.
24. Wikle, C. K. 2002. Spatial modeling of count data: a case study in modelling breeding bird survey data on large spatial domains. Pages 199-209 in A. Lawson and D. Denison, editors. Spatial cluster modelling. Chapman and Hall, London, UK.
25. Wikle, C. K. 2001. A kernel-based spectral approach for spatiotemporal dynamic models. Pages 167-180 in Proceedings of the first Spanish workshop on spatiotemporal modelling of environmental processes (METMA). Benicassim, Castellon, Spain. 28-31 October 2001. Publicacions de lat Universitat Jaume I, Castello de la Plana, Spain.
26. Wikle, C. K., L. M. Berliner, and N. Cressie. 1998. Hierarchical Bayesian space time models. Journal of Environmental and Ecological Statistics 5:117-154.
27. Wikle, C. K., L. M. Berliner, and R. F. Milliff. 2003. Hierarchical Bayesian approach to boundary value problems with stochastic boundary conditions. Monthly Weather Review. 131, in press.
28. Wikle, C. K., and N. Cressie. 1999. A dimension reduction approach to space-time Kalman filtering. Biometrika 86: 815-829.
29. Wikle, C. K., R. F. Milliff, D. Nychka, and L. M. Berliner. 2001. Spatiotemporal hierarchical Bayesian modeling: tropical ocean surface winds. Journal of the American Statistical Association 96:382-397.