SEARCH

SEARCH BY CITATION

References

  • Benioff, R., S. Guill, and J. Lee (1996), Vulnerability and Adaptation Assessments: An International Handbook, Kluwer Acad., Dordrecht, Netherlands.
  • Bhuyan, J. N., V. V. Raghavan, and K. E. Venkatesh (1991), Genetic algorithm for clustering with an ordered representation, in Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 408415, Morgan Kaufmann, San Francisco, Calif.,
  • Bonan, G. B., and S. Levis (2006), Evaluating aspects of the community land and atmosphere models (CLM3 and CAM3) using a dynamic global vegetation model, J. Clim., 19, 22902301, doi:10.1175/JCLI3741.1.
  • Bonan, G. B., S. Levis, L. Kergoat, and K. W. Oleson (2002), Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models, Global Biogeochem. Cycles, 16(2), 1021, doi:10.1029/2000GB001360.
  • Bonan, G. B., S. Levis, S. Sitch, M. Vertenstein, and K. W. Oleson (2003), A dynamic global vegetation model for use with climate models: Concepts and description of simulated vegetation dynamics, Global Change Biol., 9, 15431566, doi:10.1046/j.1365-2486.2003.00681.x.
  • Box, E. O. (1995), Factors determining distribution of tree species and plant functional types, Vegetatio, 121, 101116, doi:10.1007/BF00044676.
  • Box, E. O. (1996), Plant functional types and climate at the global scale, J. Veg. Sci., 7, 309320, doi:10.2307/3236274.
  • Calinski, R. B., and J. A. Harabasz (1974), A dendrite method for cluster analysis, Commun. Stat., 3, 127.
  • Chapin, F. S.III, S. Bret-Harte, S. E. Hobbie, and H. Zhong (1996), Plant functional Types as predictors of transient response of Arctic vegetation to global change, J. Veg. Sci., 7, 347358, doi:10.2307/3236278.
  • Cooper, M. C., and G. W. Milligan (1988), The effect of error on determining the number of clusters, in Data, Expert Knowledge, and Decisions: An Interdisciplinary Approach With Emphasis on Marketing Applications, edited by W. Gaul, and M. Schader, pp. 319328, Springer, New York.
  • Cramer, W., et al. (2001), Global response of terrestrial ecosystem structure and function to CO2 and climate change: Results from six dynamic global vegetation models, Global Change Biol., 7, 357373, doi:10.1046/j.1365-2486.2001.00383.x.
  • Dai, Y., et al. (2003), The common land model, Bull. Am. Meteorol. Soc., 84(8), 10131023, doi:10.1175/BAMS-84-8-1013.
  • Díaz, S., and M. Cabido (1997), Plant functional types and ecosystem function in relation to global change, J. Veg. Sci., 8, 463474, doi:10.2307/3237198.
  • Duda, R. O., and P. E. Hart (1973), Pattern Classification and Scene Analysis, John Wiley, New York.
  • Feng, Y., and G. Hamerly (2006), PG-means: Learning the number of clusters in data, paper presented at Twentieth Annual Conference on Neural Information Processing Systems, Neural Inf. Process. Syst., Vancouver, B. C., Canada.
  • Gitay, H., I. R. Noble, and J. H. Connell (1999), Deriving functional types for rain-forest trees, J. Veg. Sci., 10, 641650.
  • Hall, L. O., B. Ozyurt, and J. C. Bezdek (1999), Clustering with a genetically optimized approach, IEEE Trans. Evol. Comput., 3, 103112.
  • Hamerly, G., and C. Elkan (2003), Learning the k in k-means, paper presented at the Seventeenth Annual Conference on Neural Information Processing Systems, Neural Inf. Process. Syst., Vancouver, B. C., Canada.
  • Hardy, A. (1996), On the number of clusters, Comput. Stat. Data Anal., 23(1), 8396, doi:10.1016/S0167-9473(96)00022-9.
  • Henderson-Sellers, A. (1994), Global terrestrial vegetation “prediction”: The use and abuse of climate and application models, Prog. Phys. Geogr., 18, 209246, doi:10.1177/030913339401800203.
  • Holdridge, L. R. (1947), Determination of world plant formations from simple climatic data, Science, 105, 367368, doi:10.1126/science.105.2727.367.
  • Holland, J. H. (1975), Adaptation in Natural and Artificial Systems, Univ. of Michigan Press, Ann Arbor.
  • Ishioka, T. (2000), Extended k-means with an efficient estimation of the number of clusters, in Intelligent Data Engineering and Automated Learning—IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents: Second International Conference, edited by K. S. Leung, L. Chan, and H. Meng, pp. 1722, Springer, New York.
  • Ishioka, T. (2005), An expansion of X-Means for automatically determining the optimal number of clusters—Progressive iterations of k-means and merging of the clusters, paper presented at the Fourth IASTED International Conference, Int. Assoc. of Sci. and Technol. for Dev., Montreal, Que. Canada.
  • Jones, D. R., and M. A. Beltramo (1991), Solving partitioning problems with genetic algorithms, in Proceedings of the Fourth International Conference on Genetic Algorithms, edited by R. K. Belew, and L. B. Booker, pp. 442450, Morgan Kaufmann, San Francisco, Calif.,
  • Krishna, K., and M. Murty (1999), Genetic K-means algorithm, IEEE Trans. Syst. Man Cybern. B Cybern., 29(3), 433439, doi:10.1109/3477.764879.
  • Lenihan, J. M., and R. P. Neilson (1993), A rule-based vegetation formation model for Canada, J. Biogeogr., 20, 615628, doi:10.2307/2845518.
  • Levis, S., G. B. Bonan, M. Vertenstein, and K. W. Oleson (2004), The Community Land Model's Dynamic Global Vegetation Model (CLM-DGVM): Technical description and user's guide, NCAR Tech. Note TN-459+IA, 50 pp., Natl. Cent. for Atmos. Res., Boulder, Colo.,
  • Lu, L., S. Lu, F. Fotouhi, Y. Deng, and S. J. Brown (2004), Incremental genetic k-means algorithm and its application in gene expression data analysis, BMC Bioinformatics, 5, 172182, doi:10.1186/1471-2105-5-172.
  • Maulik, U., and S. Bandyopadhyay (2000), Genetic algorithm based clustering technique, Pattern Recognit., 33, 14551465, doi:10.1016/S0031-3203(99)00137-5.
  • Milligan, G. W., and M. C. Cooper (1985), An examination of procedures for determining the number of clusters in a data set, Psychometrika, 50(2), 159179, doi:10.1007/BF02294245.
  • Neilson, R. P., and D. Marks (1994), A global perspective of regional vegetation and hydrologic sensitivities from climatic change, J. Veg. Sci., 5, 715730, doi:10.2307/3235885.
  • Noble, I. R., and H. Gitay (1996), A functional classification for predicting the dynamics of landscapes, J. Veg. Sci., 7(3), 329336, doi:10.2307/3236276.
  • Pelleg, D., and A. Moore (2000), X-means: Extending k-means with efficient estimation of the number of clusters, in Proceedings of the 17th International Conference on Machine Learning, pp. 727734, Morgan Kaufmann, San Francisco, Calif.,
  • Prentice, K. C. (1990), Bioclimatic distribution of vegetation for general circulation model studies, J. Geophys. Res., 95(D8), 11,81111,830, doi:10.1029/JD095iD08p11811.
  • Prentice, K. C., and I. Fung (1990), The sensitivity of terrestrial carbon storage to climate change, Nature, 346, 4851, doi:10.1038/346048a0.
  • Prentice, I. C., A. Cramer, S. P. Harrison, R. Leemans, R. A. Monserud, and A. M. Solomon (1992), A global biome model based on plant physiology and dominance, soil properties and climate, J. Biogeogr., 19, 117134, doi:10.2307/2845499.
  • Qian, T., A. G. Dai, K. E. Trenberth, and K. W. Oleson (2006), Simulation of global land surface conditions from 1948 to 2004. Part I: Forcing data and evaluations, J. Hydrometeorol., 7, 953975, doi:10.1175/JHM540.1.
  • Ray, S., and R. Turi (1999), Determination of number of clusters in k-means clustering and application in colour image segmentation, paper presented at the 4th International Conference on Advances in Pattern Recognition and Digital Techniques, Int. Assoc. for Pattern Recog., Calcutta, India.
  • Rousseeuw, P. J. (1987), Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 5365.
  • Rudolph, G. (1994), Convergence properties of canonical genetic algorithms, IEEE Trans. Neural Networks, 5(1), 96101.
  • Sarle, W. S. (1983), Cubic clustering criterion, SAS Tech. Rep. A-108, SAS Inst. Inc., Cary, N. C.,
  • SAS Institute Inc. (1999), SAS/STAT User's Guide, version 8, Cary, N. C.,
  • Sitch, S., et al. (2003), Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Global Change Biol., 9, 161185, doi:10.1046/j.1365-2486.2003.00569.x.
  • Smith, T. M., H. H. Shugart, and F. I. Woodward (1997), Plant Functional Types, Their Relevance to Ecosystem Properties and Global Change, Cambridge Univ. Press, Cambridge, U.K.,
  • Tibshirani, R., G. Walther, and T. Hastie (2001), Estimating the number of clusters in a dataset via the gap statistic, J. R. Stat. Soc., Ser. B, 63, 411423.
  • Wang, A., and D. T. Price (2007), Estimating global distribution of boreal, temperate, and tropical tree plant functional types using clustering techniques, J. Geophys. Res., 112, G01024, doi:10.1029/2006JG000252.
  • Wang, D. G., G. L. Wang, and E. N. Anagnostou (2005), Use of satellite-based precipitation observation in improving the parameterization of canopy hydrological processes in land surface model, J. Hydrometeorol., 6, 745763, doi:10.1175/JHM438.1.
  • Welling, M., and K. Kurihara (2006), Bayesian k-means as a ‘maximization-expectation’ algorithm, paper presented at SIAM Conference on Data Mining SDM06, Soc. for Ind. and Appl. Mech., Bethesda, Md.,
  • Woodward, F. I. (1987), Climate and Plant Distribution, Cambridge Univ. Press, Cambridge, U.K.
  • Woodward, F. I., and C. K. Kelly (1997), Plant functional types: Towards a definition by environmental constraints, in Plant Functional Types, Their Relevance to Ecosystem Properties and Global Change, edited by T. M. Smith, H. H. Shugart, and F. I. Woodward, pp. 4765, Cambridge Univ. Press, Cambridge, U.K.,
  • Yates, D. N., T. G. F. Kittel, and R. F. Cannon (2000), Comparing the correlative Holdridge model to mechanistic biogeographical models for assessing vegetation distribution response to climatic change, Clim. Change, 44, 5987, doi:10.1023/A:1005495908758.
  • Zeng, X. D., X. Zeng, and M. Barlage (2007), Growing temperate shrubs over arid and semiarid regions in the NCAR Dynamic Global Vegetation Model (CLM-DGVM), Global Biogeochem. Cycles, doi:10.1029/2007GB003014, in press.