Ecological forecasting and data assimilation in a data-rich era

Ecological Applications

Volumn 21, Issue 5, 2011, Pages 1429-1442

  Luo, Yiqi ^a   Ogle, Kiona ^b   Tucker, Colin ^b   Fei, Shenfeng ^a   Gao, Chao ^a   LaDeau, Shannon ^c   Clark, James S ^d   Schimel, David S ^e  

^a UNIVERSITY OF OKLAHOMA   (United States)

^b UNIVERSITY OF WYOMING   (United States)

^c CARY INSTITUTE OF ECOSYSTEM STUDIES   (United States)

^d DUKE UNIVERSITY   (United States)

^e NEON   (United States)

Author keywords

Data assimilation; Data model fusion; Ecological forecasting; Inverse analysis; Optimization; Predictions; Prognosis; Projections

Indexed keywords

DATA ASSIMILATION; ECOLOGICAL MODELING; EXPERIMENTAL STUDY; FORECASTING METHOD; GLOBAL CHANGE; INVERSE ANALYSIS; NATURAL RESOURCE; OPTIMIZATION; PREDICTION; QUANTITATIVE ANALYSIS; RESOURCE MANAGEMENT; VOLUME;

ARTICLE; COMPUTER SIMULATION; ECOLOGY; FORECASTING; METHODOLOGY; THEORETICAL MODEL; TIME;

COMPUTER SIMULATION; ECOLOGY; FORECASTING; MODELS, THEORETICAL; TIME FACTORS;

EID: 79953846193     PISSN: 10510761     EISSN: None     Source Type: Journal    
DOI: 10.1890/09-1275.1     Document Type: Article

References (113)

1. Akimoto, H. 2003. Global air quality and pollution. Science 302:1716-1719. (Pubitemid 37505727)
2. Anderson, R. M., and R. M. May. 1979. Population biology of infectious diseases. Part 1. Nature 280:361-367.
3. Arhonditsis, G. B D. Papantou, W. T. Zhang, G. Perhar, E. Massos, and M. L. Shi. 2008. Bayesian calibration of mechanistic aquatic biogeochemical models and benefits for environmental management. Journal of Marine Systems 73:8-30.
4. Baker, D. F H. Bosch, S. C. Doney, and D. S. Schimel. 2008. Carbon source/sink information provided by column CO2 measurements from the Orbiting Carbon Observatory. Atmospheric Chemistry and Physics Discussions 8(6):20051-20112.
5. Baldocchi, D., et al. 2001. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society 82:2415-2434. (Pubitemid 33642006)
6. Beaumont, L. J L. Hughes, and M. Poulsen. 2005. Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species' current and future distributions. Ecological Modelling 186:250-269. (Pubitemid 40902339)
7. Beaumont, L. J A. J. Pitman, M. Poulsen, and L. Hughes. 2007. Where will species go? Incorporating new advances in climate modelling into projections of species distributions. Global Change Biology 13:1368-1385. (Pubitemid 47079772)
8. Beerling, D. J F. I. Woodward, M. Lomas, and A. J. Jenkins. 1997. Testing the responses of a dynamic global vegetation model to environmental change: a comparison of observations and predictions. Global Ecology and Biogeography 6:439-450.
9. Bjornstad, O. N B. F. Finkenstadt, and T. G. Bryan. 2002. Dynamics of measles epidemics: estimating scaling of transmission rates using a time series SIR model. Ecological Monographs 72:169-184. (Pubitemid 36198821)
10. Borsuk, M. E C. A. Stow, and K. H. Reckhow. 2004. A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecological Modelling 173:219-239. (Pubitemid 38334738)
11. Braswell, B. H W. J. Sacks, E. Linder, and D. S. Schimel. 2005. Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations. Global Change Biology 11:335-355. (Pubitemid 40273703)
12. Carvalhais, N., et al. 2008. Implications of the carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval. Global Biogeochemical Cycles 22:GB2007.
13. Chaves, L. F., and M. Pascual. 2007. Comparing models for early warning systems of neglected tropical diseases. PLoS Neglected Tropical Diseases 1:e33.
14. Chen, M S. Liu, L. L. Tieszen, and D. Y. Hollinger. 2008a. An improved state-parameter analysis of ecosystem models using data assimilation. Ecological Modelling 219:317-326.
15. Chen, X. Y Y. Rubin, S. Y. Ma, and D. Baldocchi. 2008b. Observations and stochastic modeling of soil moisture control on evapotranspiration in a Californian oak savanna. Water Resources Research 44.
16. Clark, J. S. 2005. Why environmental scientists are becoming Bayesians. Ecology Letters 8:2-14. (Pubitemid 40172132)
17. Clark, J. S P. Agarwal, D. M. Bell, P. G. Flikkema, A. Gelfand, X. Nguyen, E. Ward, and J. Yang. 2011. Inferential ecosystem models, from network data to prediction. Ecological Applications 21:1523-1536.
18. Clark, J. S., et al. 2010. High-dimensional coexistence based on individual variation: a synthesis of evidence. Ecological Monographs 80:569-608.
19. Clark, J. S., and O. Bjornstad. 2004. Population time series: process variability, observation errors, missing values, lags, and hidden states. Ecology 85:3140-3150. (Pubitemid 39548052)
20. Clark, J. S., et al. 2001. Ecological forecast: an emerging imperative. Science 293:657-660.
21. Clark, J. S S. LaDeau, and I. Ibanez. 2004. Fecundity of trees and the colonization-competition hypothesis. Ecological Monographs 74:415-442. (Pubitemid 39182902)
22. Clark, J. S M. Lewis, J. S. McLachlan, and J. Hille Ris Lambers. 2003. Estimating population spread: What can we forecast and how well? Ecology 84:1979-1988. (Pubitemid 37435672)
23. Clark, J. S M. Wolosin, M. Dietze, I. Ibanez, S. LaDeau, M. Welsh, and B. Kloeppel. 2007. Tree growth inference and prediction from diameter censuses and ring widths. Ecological Applications 17:1942-1953. (Pubitemid 351328166)
24. Clein, J. S B. L. Kwiatkowski, A. D. McGuire, J. E. Hobbie, E. B. Rastetter, J. M. Melillo, and D. W. Kicklighter. 2000. Modelling carbon responses of tundra ecosystems to historical and projected climate: a comparison of a plot and a global-scale ecosystem model to identify process-based uncertainties. Global Change Biology 6:127-140. (Pubitemid 32978780)
25. Cook, B. I T. M. Smith, and M. E. Mann. 2005. The North Atlantic Oscillation and regional phenology prediction over Europe. Global Change Biology 11:919-926. (Pubitemid 40865255)
26. Cox, P. M R. A. Betts, M. Collins, P. P. Harris, C. Huntingford, and C. D. Jones. 2004. Amazonian forest dieback under climate-carbon cycle projections for the 21st century. 78:137-156.
27. Cox, P. M R. A. Betts, C. D. Jones, S. A. Spall, and I. J. Totterdell. 2000. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408:184-187.
28. Crozier, L. G R. W. Zabel, and A. F. Hamlett. 2008. Predicting differential effects of climate change at the population level with life-cycle models of spring Chinook salmon. Global Change Biology 14:236-249.
29. Currie, D. J. 2001. Projected effects of climate change on patterns of vertebrate and tree species richness in the conterminous United States. Ecosystems 4:216-225. (Pubitemid 32516207)
30. D'Antonio, C. M., and P. M. Vitousek. 1992. Biological invasions by exotic grasses, the grass fire cycle, and global change. Annual Review of Ecology and Systematics 23:63-87. (Pubitemid 24411617)
31. Dormann, C. F., et al. 2008. Prediction uncertainty of environmental change effects on temperate European biodiversity. Ecology Letters 11:235-244. (Pubitemid 351172048)
32. Dowd, M. 2007. Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo. Journal of Marine Systems 68:439-456. (Pubitemid 350086919)
33. Evensen, G. 2007. Data assimilation: the ensemble Kalman filter. Springer, Berlin, Germany.
34. Ferguson, N. M C. A. Donnelly, and R. M. Anderson. 2001. Transmission intensity and impact of control policies on the foot and mouth epidemic in Great Britain. Nature 413:542-528.
35. Ferrari, M. J R. F. Grais, N. Bharti, J. K. Conlan, O. N. Bjornstad, L. J. Wolfson, P. J. Guerin, A. Djibo, and B. T. Grenfell. 2008. The dynamics of measles in sub-Saharan Africa. Nature 451:679-684. (Pubitemid 351220562)
36. Fisher, R. A. 1959. Statistical methods and scientific inference. Second edition. Oliver and Boyd, Edinburgh, UK.
37. Foley, J. A S. Levis, I. C. Prentice, D. Pollard, and S. L. Thompson. 1998. Coupling dynamic models of climate and vegetation. Global Change Biology 4:561-579. (Pubitemid 28393158)
38. Forrester, J. W. 1961. Industry dynamics. MIT Press. Cambridge, Massachusetts, USA.
39. Fox, A. M M. Williams, A. D. Richardson, D. Cameron, J. Gove, M. Reichstein, T. Quaife, D. Ricciuto, E. Tomelleri, C. M. Trudinger, and M. T. Van Wijk. 2009. The REFLEX project: comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data. Agricultural and Forest Meteorology 149:1597-1615.
40. 4MIP model intercomparison. Journal of Climate 19:3337-3353. (Pubitemid 44177898)
41. Gao, C H. Wang, E. Weng, S. Lakshmivarahan, Y. Zhang, and Y. Luo. 2011. Assimilation of multiple data sets with the ensemble Kalman filter to improve forecasts of forest carbon dynamics. Ecological Applications 21:1461-1473.
42. Gelb, A. 1974. Applied optimal estimation. MIT Press. Cambridge, Massachusetts, USA.
43. Glass, M M. Dragunow, and R. L. M. Faull. 2000. The pattern of neurodegeneration in Huntington's disease: a comparative study of cannabinoid, dopamine, adenosine and GABA (A) receptor alterations in the human basal ganglia in Huntington's disease. Neuroscience 97:505-519. (Pubitemid 30320757)
44. Gleick, P. H. 2002. The world's water 2002-2003: the biennial report on freshwater resources. Island Press, Washington, D. C., USA.
45. Grenfell, B. T O. N. Bjornstad, and J. Kappey. 2001. Travelling waves and spatial hierarchies in measles epidemics. Nature 414:716-723. (Pubitemid 34000763)
46. Hanson, P. J., et al. 2004. Oak forest carbon and water simulations: model intercomparisons and evaluations against independent data. Ecological Monographs 74:443-489. (Pubitemid 39182903)
47. Hastings, W. K. 1970. Monte Carlo sampling methods using Markov chain and their applications. Biometrika 57:97-109.
48. Heinz Report. 2008. The state of the nation's ecosystems. Measuring the land, waters, and living resources of the United States. Island Press, Washington, D. C., USA.
49. Hijmans, R. J., and C. H. Graham. 2006. The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology 12:2272-2281. (Pubitemid 44857703)
50. Hood, S. M C. W. Mchugh, K. C. Ryan, E. Reinhardt, and S. L. Smith. 2007. Evaluation of a post-fire tree mortality model for western USA conifers. International Journal of Wildland Fire 16:679-689. (Pubitemid 350273694)
51. Jones, C C. McConnell, K. Coleman, P. Cox, P. Falloon, D. Jenkinson, and D. Powlson. 2005. Global climate change and soil carbon stocks; predictions from two contrasting models for the turnover of organic carbon in soil. Global Change Biology 11:154-166. (Pubitemid 40134052)
52. Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82:35-45.
53. Kalnay, E. 2003. Atmospheric modeling, data assimilation and predictability. Cambridge University Press, Cambridge, UK.
54. Kearney, M., and W. Porter. 2009. Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges. Ecology Letters 12:334-350.
55. Keeling, M. J M. E. J. Woolhouse, R. M. May, G. Davies, and B. T. Grenfell. 2003. Modelling vaccination strategies against foot-and-mouth disease. Nature 421:136-142. (Pubitemid 36090974)
56. Keeling, M. J M. E. J. Woolhouse, D. J. Shaw, L. Matthews, M. Chase-Topping, D. T. Haydon, S. J. Cornell, J. Kappey, J. Wilesmith, and B. T. Grenfell. 2001. Dynamics of the 2001 UK foot and mouth epidemic: stochastic dispersal in a heterogeneous landscape. Science 294:813-817. (Pubitemid 33032092)
57. Kermack, W. O., and A. G. McKendrick. 1933. Contributions to the mathematical theory of epidemics. Part III. Proceedings of the Royal Society A 141:94-122.
58. Kirilenko, A B. Chivoiu, J. Crick, A. Ross-Davis, K. Schaaf, G. Shao, V. Singhania, and R. Swihart. 2007. An internetbased decision support tool for non-industrial private forest landowners. Environmental Modeling and Software 22:1498-1508. (Pubitemid 46561423)
59. Klemedtsson, L P. E. Jansson, D. Gustafsson, L. Karlberg, P. Weslien, K. von Arnold, M. Ernfors, O. Langvall, and A. Lindroth. 2008. Bayesian calibration method used to elucidate carbon turnover in forest on drained organic soil. Biogeochemistry 89:61-79.
60. Knapp, A. K., and M. D. Smith. 2001. Variation among biomes in temporal dynamics of aboveground primary production. Science 291:481-484. (Pubitemid 32097071)
61. Krishnamurti, T. N C. M. Kishtawal, T. E. LaRow, D. R. Bachiochi, Z. Zhang, C. E. Williford, S. Gadgil, and S. Surendran. 1999. Improved weather and seasonal climate forecasts from multimodel superensemble. Science 285:1548-1550. (Pubitemid 29420575)
62. Law, T W. T. Zhang, J. Y. Zhao, and G. B. Arhonditsis. 2009. Structural changes in lake functioning induced from nutrient loading and climate variability. Ecological Modelling 220:979-997.
63. Liao, C. Z R. H. Peng, Y. Q. Luo, X. H. Zhou, X. W. Wu, C. M. Fang, J. K. Chen, and B. Li. 2008. Altered ecosystem carbon and nitrogen cycles by plant invasion: a metaanalysis. New Phytologist 177:706-714.
64. Liu, M H. L. He, G. R. Yu, Y. Q. Luo, X. M. Sun, and H. M. Wang. 2009. Uncertainty analysis of CO2 flux components in subtropical evergreen coniferous plantation. Science in China Series D: Earth Sciences 52:257-268.
65. 2 fluxes at regional scale using the maximum likelihood ensemble filter. Journal of Geophysical Research-Atmospheres 113:D20110.
66. Lotka, A. J. 1924. Elements of physical biology. Williams and Wilkins, New York, New York, USA.
67. Luo, Y L. White, J. Canadell, E. DeLucia, D. Ellsworth, A. Finzi, J. Lichter, and W. Shclesinger. 2003. Sustainability of terrestrial carbon sequestration: a case study in Duke Forest with inversion approach. Global Biogeochemical Cycles 17(1):1021.
68. Luo, Y., and X. Zhou. 2006. Soil respiration and the environment. Academic Press/Elsevier, San Diego, California, USA.
69. May, R. M. 1981. Theoretical ecology: principles and applications. Sinauer, Sunderland, Massachusetts, USA.
70. May, R. M. 2001. Stability and complexity in model ecosystems. Princeton University Press, Princeton, New Jersey, USA.
71. McGuire, A. D J. S. Clein, J. M. Melillo, D. W. Kicklighter, R. A. Meier, C. J. Vorosmarty, and M. C. Serreze. 2000. Modelling carbon responses of tundra ecosystems to historical and projected climate: sensitivity of pan-Arctic carbon storage to temporal and spatial variation in climate. Global Change Biology 6:141-159. (Pubitemid 32978781)
72. Metropolis, N A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller. 1953. Equation of state calculation by fast computer machines. Journal of Chemical Physics 21:1087-1092.
73. Mo, X. G J. M. Chen, W. M. Ju, and T. A. Black. 2008. Optimization of ecosystem model parameters through assimilating eddy covariance flux data with an ensemble Kalman filter. Ecological Modelling 217:157-173.
74. Monson, R. K D. L. Lipson, S. P. Burns, A. A. Turnipseed, A. C. Delany, M. W. Williams, and S. K. Schmidt. 2006. Winter forest soil respiration controlled by climate and microbial community composition. Nature 439:711-714. (Pubitemid 43237615)
75. Moore, D. J. P J. Hu, W. J. Sacks, D. S. Schimel, and R. K. Monson. 2008. Estimating transpiration and the sensitivity of carbon uptake to water availability in a subalpine forest using a simple ecosystem process model informed by measured net CO2 and H2O fluxes. Agricultural and Forest Meteorology 148:1467-1477.
76. Morin, X D. Viner, and I. Chuine. 2008. Tree species range shifts at a continental scale: new predictive insights from a process-based model. Journal of Ecology 96:784-794. (Pubitemid 351822123)
77. Nemani, R. R C. D. Keeling, H. Hashimoto, W. M. Jolly, S. C. Piper, C. J. Tucker, R. B. Myneni, and S. W. Running. 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300:1560-1563. (Pubitemid 36682885)
78. Ogle, K. 2009. Hierarchical Bayesian statistics: merging experimental and modeling approaches in ecology. Ecological Applications 19:577-581.
79. Ogle, K., and S. W. Pacala. 2009. A modeling framework for inferring tree growth and allocation from physiological, morphological, and allometric traits. Tree Physiology 29:587-605.
80. Ourmieres, Y P. Brasseur, M. Levy, J. M. Brankart, and J. Verron. 2009. On the key role of nutrient data to constrain a coupled physical-biogeochemical assimilative model of the North Atlantic Ocean. Journal of Marine Systems 75:100-115.
81. Pacala, S. W C. D. Canham, and J. A. Silander. 1993. Forest models defined by field-measurements. 1. The design of a northeastern forest simulator. Canadian Journal of Forest Research 23:1980-1988. (Pubitemid 24412719)
82. 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. (Pubitemid 19317426)
83. Parton, W. J J. M. O. Scurlock, D. S. Ojima, T. G. Gilmanov, R. J. Scholes, D. S. Schimel, T. Kirchner, J.-C. Menaut, T. Seastedt, E. Garcia Moya, Apinan Kamnalrut, and J. L. Kinyamario. 1993. Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global Biogeochemical Cycles 7:785-809. (Pubitemid 24399285)
84. Pascual, M. A., and P. Kareiva. 1996. Predicting the outcome of competition using experimental data: maximum likelihood and Bayesian approaches. Ecology 77:337-349.
85. Prasad, A. M L. R. Iverson, and A. Liaw. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181-199.
86. Prihodko, L A. S. Denning, N. P. Hannan, I. Baker, and K. Davis. 2008. Sensitivity, uncertainty and time dependence of parameters in a complex land surface model. Agricultural and Forest Meteorology 148:268-287.
87. Quaife, T P. Lewis, M. De Kauwe, M. Williams, B. E. Law, M. Disney, and P. Bowyer. 2008. Assimilating canopy reflectance data into an ecosystem model with an ensemble Kalman filter. Remote Sensing of Environment 112:1347-1364. (Pubitemid 351375229)
88. Raupach, M. R P. J. Rayner, D. J. Barrett, R. S. Defries, M. Heimann, D. S. Ojima, S. Quegan, and C. C. Schmullius. 2005. Model-data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications. Global Change Biology 11:378-397.
89. 2O exchange in three Mediterranean ecosystems. Journal of Geophysical Research-Atmospheres 108:4726.
90. Ribbens, E J. A. Silander, and S. W. Pacala. 1994. Seedling recruitment in forests: calibrating models to predict patterns of tree seedling dispersion. Ecology 75:1794-1806. (Pubitemid 2134560)
91. 2 exchange in a northern Wisconsin forest using a Bayesian model calibration. Agricultural and Forest Meteorology 148:309-327. (Pubitemid 351215063)
92. Ricciuto, D. M K. J. Davis, and K. Keller. 2008b. A Bayesian calibration of a simple carbon cycle model: the role of observations in estimating and reducing uncertainty. Global Biogeochemical Cycles 22:GB2030.
93. Schimel, D. S., et al. 1997. Continental scale variability in ecosystem processes: Models, data, and the role of disturbance. Ecological Monographs 67:251-271.
94. Schwartz, M. W L. R. Iverson, and A. M. Prasad. 2001. Predicting the potential future distribution of four tree species in Ohio using current habitat availability and climatic forcing. Ecosystems 4:568-581. (Pubitemid 33037419)
95. Smith, K. F A. P. Dobson, F. E. McKenzie, L. A. Real, D. L. Smith, and M. L. Wilson, 2005. Ecological theory to enhance infectious disease control and public health policy. Frontiers in Ecology and the Environment 3:29-37. (Pubitemid 40335933)
96. Solomon, S., et al. 2007. Technical summary. In Climate change 2007: the physical science basis. Contribution of Working Group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. S Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller, editors. Cambridge University Press, Cambridge, UK.
97. Spitz, Y. H J. R. Moisan, M. R. Abbott, and J. G. Richman. 1998. Data assimilation and a pelagic ecosystem model: parameterization using time series observations. Journal of Marine Systems 16:51-68. (Pubitemid 28407984)
98. Svensson, M P. E. Jansson, D. Gustafsson, D. B. Kleja, O. Langvall, and A. Lindroth. 2008. Bayesian calibration of a model describing carbon, water and heat fluxes for a Swedish boreal forest stand. Ecological Modelling 213:331-344.
99. Tang, J. Y., and Q. L. Zhuang. 2008. Equifinality in parameterization of process-based biogeochemistry models: a significant uncertainty source to the estimation of regional carbon dynamics. Journal of Geophysical Research-Biogeosciences. 113:G04010.
100. Todling, R. 2000. Estimation theory and atmospheric data assimilation. Pages 49-65 in P. S. Kasibhatla, editor. Inverse methods in global biogeochemical cycles. American Geophysical Monograph 114. American Geophysical Union, Washington, D. C., USA.
101. Vallecillo, S L. Brotons, and W. Thuiller. 2009. Dangers of predicting bird species distributions in response to land-cover changes. Ecological Applications 19:538-549.
102. Verhulst, P. F. 1838. Notice sur la loi que la population poursuit dans son accroissement. Correspondance mathematique et physique 10:113-121.
103. Vitousek, P. M H. A. Mooney, J. Lubchenco, and J. M. Melillo. 1997. Human domination of Earth's ecosystems. Science 277:494-499. (Pubitemid 27368986)
104. Volterra, V. 1926. Fluctuations in the abundance of a species considered mathematically. Nature 118:558-560.
105. Vukicevic, T B. Braswell, and D. S. Schimel. 2001. A diagnostic study of temperature controls on global terrestrial carbon exchange. Tellus B 53:150-170. (Pubitemid 32804563)
106. Weng, E., and Y. Luo. 2011. Relative information contributions of model vs. data to short-and long-term forecasts of forest carbon dynamics. Ecological Applications 21:1490-1505.
107. Williams, M., et al. 2009. Improving land surface models with FLUXNET data. Biogeosciences 6:2785-2835.
108. Williams, M P. A. Schwarz, B. E. Law, J. Irvine, and M. R. Kurpius. 2005. An improved analysis of forest carbon dynamics using data assimilation. Global Change Biology 11:89-105. (Pubitemid 40134047)
109. Williams, S. E L. P. Shoo, J. L. Isaac, A. A. Hoffmann, and G. Langham. 2008. Towards an integrated framework for assessing the vulnerability of species to climate change. PLoS Biology 6(12):e325.
110. Williford, C. E T. N. Krishnamurti, R. C. Torres, S. Cocke, Z. Christidis, and T. S. V. Kumar. 2003. Real-time multimodel superensemble forecasts of Atlantic tropical systems of 1999. Monthly Weather Review 131:1878-1894. (Pubitemid 37124280)
111. Xu, T L. White, D. Hui, and Y. Luo. 2006. Probabilistic inversion of a terrestrial ecosystem model: analysis of uncertainty in parameter estimation and model prediction. Global Biogeochemical Cycles 20:GB2007.
112. Zhao, Q., and X. Lu. 2008. Parameter estimation in a threedimensional marine ecosystem model using the adjoint technique. Journal of Marine Systems 74:443-452.
113. Zhou, T., and Y. Luo. 2008. Spatial patterns of ecosystem carbon residence time and NPP-driven carbon uptake in the conterminous United States. Global Biogeochemical Cycles 22:GB3032.