Sensitivity of ecological models to their climate drivers: Statistical ensembles for forcing

Ecological Applications

Volumn 16, Issue 1, 2006, Pages 99-116

  Fuentes, Montserrat ^a   Kittel, Timothy G F ^b,c   Nychka, Doug ^d  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF COLORADO   (United States)

^c Veterinary Dentistry and Oral Surgery   (United States)

^d NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

Bayesian inference; Ecological numerical models; Kriging; Nonstationary process; Spatial statistics; Variogram

Indexed keywords

ECOLOGICAL MODELING; ECOSYSTEM DYNAMICS; KRIGING; NUMERICAL MODEL; SPATIAL ANALYSIS; VARIOGRAM;

ANIMAL; BIOLOGICAL MODEL; CLIMATE; COMPUTER SIMULATION; CONFERENCE PAPER; ECOLOGY; ECOSYSTEM; HUMAN; POPULATION DENSITY; SENSITIVITY AND SPECIFICITY; THEORETICAL MODEL; TIME; UNCERTAINTY; UNITED STATES;

ANIMALS; CLIMATE; COMPUTER SIMULATION; ECOLOGY; ECOSYSTEM; HUMANS; MODELS, BIOLOGICAL; MODELS, THEORETICAL; POPULATION DENSITY; SENSITIVITY AND SPECIFICITY; TIME FACTORS; UNCERTAINTY; UNITED STATES;

EID: 33645098590     PISSN: 10510761     EISSN: 10510761     Source Type: Journal    
DOI: 10.1890/04-1157     Document Type: Conference Paper

References (33)

1. Axelsson, O. 1985. A survey of preconditioned iterative methods for linear systems of equations. BIT 25:166-187.
2. Bayarri, M. J., J. O. Berger, D. Higdon, M. C. Kennedy, A. Kottas, R. Paulo, and J. Sacks. 2002. A framework for validation of computer models. Technical report number 128. National Institute of Statistical Science, Research Triangle Park, North Carolina, USA.
3. Cramer, W. 1997. Modeling the possible impact of climate change on broad-scale vegetation structure: examples from northern Europe. Pages 381-401 in W. C. Oechel, T. Callaghan, T. Gilmanov, J. I. Holten, B. Maxwell, U. Molau, and B. Sveinbjornsson, editors. Global change and arctic terrestrial ecosystems. Springer-Verlag, New York, New York, USA.
4. Cressie, N. A. C. 1985a. When are relative semivariograms useful in geostatistics? Journal of the International Association of Mathematical Geology 17:693-702.
5. Cressie, N. A. C. 1985b. Fitting variogram models by weighted least squares. Journal of the International Association of Mathematical Geology 17:563-586.
6. Cressie, N. A. C. 1993. Statistics for spatial data. Revised edition. John Wiley and Sons, New York, New York, USA.
7. Fuentes, M., P. Guttorp, and P. Challenor. 2003. Statistical assessment of numerical models. International Statistical Review 71:201-221.
8. Fuentes, M., and A. E. Raftery. 2005. Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models. Biometrics 61:36-45.
9. Gelfand, A. E., and A. F. M. Smith. 1990. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85:398-409.
10. Gilks, W. R., S. Richardson, and D. J. Spiegalhalter. 1996. Markov chain Monte Carlo in practice. Chapman and Hall, New York, New York, USA.
11. Golub, G. H., and C. F. Van Loan. 1989. Matrix computations. Second edition. The Johns Hopkins University Press, Baltimore, Maryland, USA.
12. Gordon, W. G., J. S. Famiglietti, N. A. Fowler, T. G. F. Kittel, and K. A. Hibbard. 2004. Validation of simulated runoff from VEMAP Phase II using observed streamflow: results from six terrestrial ecosystem models. Ecological Applications 14:527-545.
13. Gribov, A., K. Krivoruchko, and J. M. Ver Hoef. 2005. Modified weighted least squares semivariogram and covariance model fitting algorithm. In M. J. Yarus and R. L. Chambers, editors. Stochastic modeling and geostatistics. AAPG computer applications in geology. Volume 2. In press.
14. Johns, C. J., D. Nychka, T. G. F. Kittel, and C. Daly. 2003. Infilling sparse records of spatial fields. Journal of the American Statistical Association 98:796-806.
15. Journel, A., and C. Huijbregts. 1978. Mining geostatistics. Academic Press, New York, New York, USA.
16. Kelly, R. H., W. J. Parton, M. D. Hartman, L. K. Stretch, D. S. Schimel, and D. S. Ojima. 2000. Intra- and interannual variability of ecosystem processes in shortgrass steppe: new model, verification, simulations. Journal of Geophysical Research: Atmospheres 105(D15):20093-20100.
17. Kennedy, M. C., and A. O'Hagan. 2000. Predicting the output from a complex computer code when fast approximations are available. Biometrika 87:1-13.
18. Kennedy, M. C., and A. O'Hagan. 2001. Bayesian calibration of computer models (with discussion). Journal of the Royal Statistical Society, Series B 63:425-464.
19. Kittel, T. G. F., et al. 2004. The VEMAP Phase 2 bioclimatic database. I: a gridded historical (20th century) climate dataset for modeling ecosystem dynamics across the conterminous United States. Climate Research 27:151-170.
20. Lapitan, R. L., and W. J. Parton. 1996. Seasonal variabilities in the distribution of the microclimatic factors and evapotranspiration in a shortgrass steppe. Agricultural and Forest Meteorology 79:113-130.
21. Lauenroth, W. K., and O. E. Sala. 1992. Long-term forage production of North American shortgrass steppe. Ecological Applications 2:397-403.
22. Matheron, G. 1971. The theory of regionalized variables and its applications. Les Cahiers du Centre de Morphologie Mathématique, Fasc. 5, Centre de Géostatistique, Fontainebleau, France.
23. National Climatic Data Center. 1996. Summary of the month - cooperative. NCDC numeric data collection: TD 3220. U.S. Department of Commerce, Washington, D.C., USA.
24. Neilson, R. P., I. C. Prentice, B. Smith, T. Kittel, and D. Viner. 1997. Simulated changes in vegetation distribution under global warming. Pages 439-456 in R. T. Watson, M. C. Zinyowera, and R. H. Moss, editors. The regional impacts of climate change. An assessment of vulnerability. Annex C, A special report of the IPCC working group II. Cambridge University Press, New York, New York, USA.
25. Oakley, J., and A. O'Hagan. 2002. Bayesian inference for the uncertinaty distribution of computer model outputs. Biometrika 89:769-784.
26. O'Hagan, A., M. C. Kennedy, and J. E. Oakley. 1999. Uncertainty analysis and other inference tools for complex computer codes. Pages 503-524 in J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, editors. Bayesian statistics 6. Oxford University Press, Oxford, UK.
27. Parton, W. J., D. S. Schimel, C. V. Cole, and D. S. Ojima. 1987. Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Science Society of America Journal 51:1173-1179.
28. Parton, W. J., D. S. Schimel, D. S. Ojima, and C. V. Cole. 1994. A general model for soil organic matter dynamics: sensitivity to litter chemistry, texture and management. Pages 147-167 in R. B. Bryant and R. W. Arnold, editors. Quantitative modeling of soil forming processes. Special Publication. Soil Science Society of America, Madison, Wisconsin, USA.
29. Sacks, J., J. Welch, T. J. Mitchell, and H. P. Wynn. 1989. Design and analysis of computer experiments. Statistical Science 4:409-435.
30. Santer, T. J., B. J. Williams, and W. Notz. 2003. The design and analysis of computer experiments. Springer, New York, New York, USA.
31. 2 and climate to carbon storage by ecosystems of the United States. Science 287:2004-2006.
32. Smith, P., et al. 1997. A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments. Geoderma 81:153-225.
33. 2 doubling. Global Biogeochemical Cycles 9:407-437.