Facilitating feedbacks between field measurements and ecosystem models

Ecological Monographs

Volumn 83, Issue 2, 2013, Pages 133-154

  LeBauer, David S ^a   Wang, Dan ^a   Richter, Katherine T ^a   Davidson, Carl C ^a   Dietze, Michael C ^a,b  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b BOSTON UNIVERSITY   (United States)

Author keywords

Bayesian approach; Ecoinformatics; Ecological forecast; Ecophysiology; Ecosystem Demography model; Ecosystem model; Meta analysis; Plant traits; Sensitivity analysis; Variance decomposition

Indexed keywords

BAYESIAN ANALYSIS; BIOLOGICAL PRODUCTION; BIOMASS ALLOCATION; DECOMPOSITION ANALYSIS; ECOPHYSIOLOGY; ECOSYSTEM FUNCTION; ECOSYSTEM MODELING; ECOSYSTEM RESPONSE; FEEDBACK MECHANISM; FIELDWORK; FINE ROOT; FORECASTING METHOD; GLOBAL CHANGE; GRASS; LEAF AREA; META-ANALYSIS; MONOCULTURE; PARAMETERIZATION; PERENNIAL PLANT; RESPIRATION; ROOT SYSTEM; SENSITIVITY ANALYSIS; TURNOVER; UNCERTAINTY ANALYSIS; VARIANCE ANALYSIS;

CARYA ILLINOINENSIS; PANICUM VIRGATUM;

EID: 84878598615     PISSN: 00129615     EISSN: 15577015     Source Type: Journal    
DOI: 10.1890/12-0137.1     Document Type: Article

References (66)

1. Albert, C. H., F. Grassein, F. M. Schurr, G. Vieilledent, and C. Violle. 2011. When and how should intraspecific variability be considered in trait-based plant ecology? Perspectives in Plant Ecology, Evolution and Systematics 13:217-225.
2. Amthor, J. 2000. The McCree-de Wit-Penning de Vries-Thornley respiration paradigms: 30 years later. Annals of Botany 86:1-20.
3. Angel, J. 2010. Historical climate data. Illinois State Climatol-ogist data for Station 118749 (Urbana). Illinois State Water Survey, Urbana, Illinois, USA. http://www.isws.illinois.edu/atmos/statecli/Summary/118740.htm
4. Ball, J., I. Woodrow, and J. Berry. 1987. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. Pages 221-224 in J. Biggins, editor. Progress in photosynthesis research. Martinus Nijhoff Publishers, Leiden, The Netherlands.
5. Bellocchi, G., M. Rivington, M. Donatelli, and K. Matthews. 2010. Validation of biophysical models: issues and methodologies. A review. Agronomy for Sustainable Development 30:109-130.
6. Beven, K. 2006. A manifesto for the equifinality thesis. Journal of Hydrology 320:18-36.
7. Beven, K., and J. Freer. 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology 249:11-29.
8. Breza, L. C., L. Souza, N. J. Sanders, and A. T. Classen. 2012. Within and between population variation in plant traits predicts ecosystem functions associated with a dominant plant species. Ecology and Evolution 2:1151-1161.
9. Cariboni, J., D. Gatelli, R. Liska, and A. Saltelli. 2007. The role of sensitivity analysis in ecological modelling. Ecological Modelling 203:167-182.
10. Casella, G., and R. L. Berger. 2001. Statistical inference. Second edition. Duxbury Press, Pacific Grove, California, USA.
11. Clark, J. S., et al. 2001. Ecological forecasts: an emerging imperative. Science 293:657-660.
12. Clark, J. S., M. Silman, R. Kern, E. Macklin, and J. HilleRisLambers. 1999. Seed dispersal near and far: patterns across temperate and tropical forests. Ecology 80:1475-1494.
13. 4 plants. Functional Plant Biology 19:519-538.
14. Denman, K., G. et al. 2007. Couplings between changes in the climate system and biogeochemistry. Pages 499-587 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. Cambridge University Press, New York, New York, USA.
15. Dietze, M. C., and A. M. Latimer. 2011. Forest simulators. Pages 307-316 in A. Hastings and L. Gross, editors. Sourcebook in theoretical ecology. University of California Press, Berkeley, California, USA.
16. Dietze, M. C., D. S. LeBauer. and R. Kooper. 2013. On improving the communication betweeen models and data. Plant, Cell and Environment, in press. http://dx.doi.org/10.1111/pce.12043
17. Dietze, M. C., et al. 2011. Characterizing the performance of ecosystem models across time scales: a spectral analysis of the North American Carbon Program site-level synthesis. Journal of Geophysical Research 116:G04029.
18. Ellison, A. M. 2010. Repeatability and transparency in ecological research. Ecology 91:2536-2539.
19. Ernst, W. H. O., E. M. Veenendaal, and M. M. Kebakile. 1992. Possibilities for dispersal in annual and perennial grasses in a savanna in Botswana. Vegetatio 102:1-11.
20. Farquhar, G. D., and T. D. Sharkey. 1982. Stomatal conductance and photosynthesis. Annual Review of Plant Physiology 33:317-345.
21. Gelman, A., and D. B. Rubin. 1992. Inference from iterative simulation using multiple sequences. Statistical Science 7:457-472.
22. Gelman, A., and K. Shirley. 2011. Inference from simulations and monitoring convergence. Pages 163-174 in S. Brooks, A. Gelman, G. Jones, and X.-L. Meng, editors. Handbook of Markov chain Monte Carlo. CRC Press, Boca Raton, Florida, USA.
23. Gill, R. A., and R. B. Jackson. 2000. Global patterns of root turnover for terrestrial ecosystems. New Phytologist 147:13-31.
24. Heaton, E. A., F. G. Dohleman, and S. P. Long. 2008. Meeting US biofuel goals with less land: the potential of Miscanthus. Global Change Biology 14:2000-2014.
25. Jackson, R. B., H. A. Mooney, and E. D. Schulze. 1997. A global budget for fine root biomass, surface area, and nutrient contents. Proceedings of the National Academy of Sciences USA 94:7362-7366.
26. Jones, M. B., M. P. Schildhauer, O. Reichman, and S. Bowers. 2006. The new bioinformatics: integrating ecological data from the gene to the biosphere. Annual Review of Ecology, Evolution, and Systematics 37:519-544.
27. Jongejans, E., and P. Schippers. 1999. Modeling seed dispersal by wind in herbaceous species. Oikos 87:362-372.
28. Justus, C., W. Hargraves, A. Mikhail, and D. Graber. 1978. Methods for estimating wind speed frequency distributions. Journal of Applied Meteorology 17:350-353.
29. Kruschke, J. 2010. Doing Bayesian data analysis. First edition. Elsevier/Academic Press, Boston, Massachusetts, USA.
30. Kubien, D. S., and R. F. Sage. 2004. Low-temperature photosynthetic performance of a C4 grass and a co-occurring C3 grass native to high latitudes. Plant, Cell and Environment 27:907-916.
31. Larocque, G. R., J. S. Bhatti, R. Boutin, and O. Chertov. 2008. Uncertainty analysis in carbon cycle models of forest ecosystems: research needs and development of a theoretical framework to estimate error propagation. Ecological Modelling 219:400-412.
32. 3 plants. Plant, Cell and Environment 18:339-355.
33. Luo, Y., K. Ogle, C. Tucker, S. Fei, C. Gao, S. LaDeau, J. S. Clark, and D. S. Schimel. 2011. Ecological forecasting and data assimilation in a data-rich era. Ecological Applications 21:1429-1442.
34. Luo, Y., E. Weng, X. Wu, C. Gao, X. Zhou, and L. Zhang. 2009. Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models. Ecological Applications 19:571-574.
35. 4-type leaf photosynthesis parameters. Plant, Cell and Environment 30:1191-1204.
36. McLaughlin, S., and L. Kszos. 2005. Development of switchgrass (Panicum virgatum) as a bioenergy feedstock in the United States. Biomass and Bioenergy 28:515-535.
37. McMahon, S. M., M. C. Dietze, M. H. Hersh, E. V. Moran, and J. S. Clark. 2009. A predictive framework to understand forest responses to global change. Annals of the New York Academy of Sciences 1162:221-236.
38. Medvigy, D., S. C. Wofsy, J. W. Munger, D. Y. Hollinger, and P. R. Moorcroft. 2009. Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2. Journal of Geophysical Research 114:1-21.
39. Mesinger, F., et al. 2006. North American Regional Reanalysis. Bulletin of the American Meteorological Society 87:343-360.
40. Michener, W. K., and M. B. Jones. 2012. Ecoinformatics: supporting ecology as a data-intensive science. Trends in Ecology and Evolution 27:85-93.
41. Miguez, F., X. Zhu, S. Humphries, G. Bollero, and S. Long. 2009. A semimechanistic model predicting the growth and production of the bioenergy crop Miscanthus giganteus: description, parameterization and validation. GCB Bioen-ergy 1:282-296.
42. Mood, A. M., F. A. Graybill, and D. C. Boes. 1974. Introduction to the theory of statistics. Third edition. McGraw-Hill, New York, New York, USA.
43. Moorcroft, P. R., G. C. Hurtt, and S. W. Pacala. 2001. A method for scaling vegetation dynamics: the Ecosystem Demography model (ED). Ecological Monographs 71:557-586.
44. 2O fluxes. Agricultural and Forest Meteorology 148:1467-1477.
45. Oehlert, G. 1992. A note on the delta method. American Statistician 46:27-29.
46. Oyarzabal, M., J. M. Paruelo, F. Pino, M. Oesterheld, and W. K. Lauenroth. 2008. Trait differences between grass species along a climatic gradient in South and North America. Journal of Vegetation Science 19:183-192.
47. Plummer, M. 2010. JAGS (Just Another Gibbs Sampler). Version 2.2.0 user manual. http://ftp.jaist.ac.jp/pub/sourceforge/m/project/mc/mcmc-jags/Manuals/2. x/jags-user-manual.pdf
48. Reich, P. B., and J. Oleksyn. 2004. Global patterns of plant leaf N and P in relation to temperature and latitude. Proceedings of the National Academy of Sciences USA 101:11001-11006.
49. 2O exchange in three Mediterranean ecosystems. Journal of Geophysical Research 108:4726.
50. Richardson, A., and D. Hollinger. 2005. Statistical modeling of ecosystem respiration using eddy covariance data: Maximum likelihood parameter estimation, and Monte Carlo simulation of model and parameter uncertainty, applied to three simple models. Agricultural and Forest Meteorology 131:191-208.
51. Saltelli, A., M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola. 2008. Global sensitivity analysis. John Wiley, Chichester, West Sussex, UK.
52. Saugier, B., J. Roy, and H. Mooney. 2001. Estimations of global terrestrial productivity: Converging toward a single number? Pages 543-557 in B. Saugier and H. Mooney, editors. Terrestrial global productivity. Academic Press, San Diego, California, USA.
53. 2 exchange across North America: Results from the North American Carbon Program site synthesis. Journal of Geophysical Research 115:G00H05.
54. Skillman, J. B. 2008. Quantum yield variation across the three pathways of photosynthesis: not yet out of the dark. Journal of Experimental Botany 59:1647-1661.
55. Stodden, V., et al. 2010. Reproducible Research 12(5):8-13. IEEE Computer Society, CS Digital Library. http://doi.ieeecomputersociety.org/10.1109/MCSE. 2010.113
56. Thompson, S., and G. Katul. 2008. Plant propagation fronts and wind dispersal: an analytical model to upscale from seconds to decades using superstatistics. American Naturalist 171:468-479.
57. Tjoelker, M. G., J. M. Craine, D. Wedin, P. B. Reich, and D. Tilman. 2005. Linking leaf and root trait syndromes among 39 grassland and savannah species. New Phytologist 167:493-508.
58. USDA NRCS. 2011. The PLANTS Database. U.S. Department of Agriculture, Natural Resources Conservation Service. http://plants.usda.gov/
59. VanLoocke, A., T. E. Twine, M. Zeri, and C. J. Bernacchi. 2012. A regional comparison of water use efficiency for miscanthus, switchgrass and maize. Agricultural and Forest Meteorology 164:82-95.
60. Violle, C., B. J. Enquist, B. J. McGill, L. Jiang, C. H. Albert, C. Hulshof, V. Jung, and J. Messier. 2012. The return of the variance: intraspecific variability in community ecology. Trends in Ecology and Evolution 27:244-252.
61. Wang, D., D. S. LeBauer, and M. C. Dietze. 2010. A quantitative review comparing the yield of switchgrass in monocultures and mixtures in relation to climate and management factors. GCB Bioenergy 2:16-25.
62. Wang, D., D. S. LeBauer, and M. C. Dietze. 2013. Predicting yields of short-rotation hybrid poplar (Populus spp.) for the contiguous USA through model-data synthesis. Ecological Applications 23:944-958.
63. Wang, D., M. W. Maughan, J. Sun, X. Feng, F. E. Miguez, D. K. Lee, and M. C. Dietze. 2011. Impacts of canopy position and nitrogen on nitrogen allocation and photosynthesis of switchgrass (Panicum virgatum L.). Aspects of Applied Biology 112:341-351.
64. Williams, M., et al. 2009. Improving land surface models with FLUXNET data. Biogeosciences Discussions 6:2785-2835.
65. Wright, I. J., et al. 2004. The worldwide leaf economics spectrum. Nature 428:821-827.
66. 3 plants - a retrospective analysis of the A/Ci curves from 109 species. Journal of Experimental Botany 44:907-920