Advertisement

Community Ecology

, Volume 1, Issue 2, pp 181–193 | Cite as

Mt Glorious revisited: secondary succession in subtropical rainforest

  • M. B. DaleEmail author
Article

Abstract

In this paper, I re-examine the subtropical rainforest succession previously studied by Williams, Lance, Webb, Tracey and Dale (1969) (WLWTD) using a clustering procedure based on the Minimal Message Length principle of induction. This principle permits the optimal number of clusters to be estimated automatically. Optimality is defined here as a trade-off between quality of fit and complexity of model, both measured in message length units.

Because of the common unit of measurement, we can assess the numerical effectiveness of the procedures adopted in the previous study and compare the results obtained by using density as against presence/absence data or the value of numeric data independent of presence/absence effects. The results also bear on the “principle of explicability” which posits that users seek interpretable results, even if they are less efficient in purely numerical terms.

The optimal density result identified 8 clusters, although these were further clustered into 3 higher level groupings. The pattern of 2 temporal stages followed by spatial segregation is clear, with extra detail concerning aberrant stands and temporal dependency in the third spatial stage also apparent. This analysis was the most effective at recovering structure in the data, of those examined.

Imposing the WLWTD analysis on density data was markedly suboptimal and even the number of clusters recognised (7) was strictly incorrect. However, by subjective interpretation WLWTD selected a number of clusters which was very close to the optimal density solution. For this reason insight gained into the processes operating was not overly compromised. The optimal density result cleans up a few corners and adds more detail but the main outlines are sufficiently clear in the subjectively assessed presence data.

The results from optimal presence/absence analysis were understandable and effective, though considerably less detailed than those obtained using the density data or those from WLWTD’s original analyses. Indeed the 3 clusters established using the presence data reflect the higher level of structure which is recognisable in the density result. Using numeric data with 0 values set to missing values, showed little of interest.

Invocation of Kodratoff’s principle of explicability, which argues for interpretability to dominate efficiency, was unnecessary since the efficient analyses were directly interpretable. The introduction of domain knowledge during the subjective interpretation in the original analysis was apparently sufficient to counter any losses due to the inefficiency of the clustering method. Given more effective clustering methods and using the density data, it becomes unnecessary.

Keyword

Clustering Explicability Minimum Message Length Prediction Rainforest Succession 

Abbreviations

MML

Minimal Message Length

N0M

Numeric data with 0 values set to missing values

WLWTD

Williams, Lance, Webb, Tracey and Dale (1969)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anand, M. 2000. Fundamentals of vegetation change: complexity rules. Acta Biotheoretica 48: 1–14.CrossRefGoogle Scholar
  2. Austin, M. P. 1970. An applied ecological example of mixed data classification. In: R. S. Anderssen and M. R. Osborne (eds.), Data Representation, Univ. Queensland Press, Brisbane. pp. 113–117.Google Scholar
  3. Barsalou, L. W. 1995. Deriving categories to achieve goals. In: A. Ram and D. B. Leake (eds.), Goal Directed Learning. MIT Press Cambridge MA. pp. 121–176.Google Scholar
  4. Boerlijst, M. C. and P. Hogeweg. 1991. Spiral wave structure in pre-biotic evolution: hypercycles stable against parasites. Physica D 48: 17–28.CrossRefGoogle Scholar
  5. Boulton, D. M. and C. S. Wallace 1970. A program for numerical classification. Comput. J. 13: 63–69.CrossRefGoogle Scholar
  6. Boulton, D. M. and C. S. Wallace. 1973. An information measure for hierarchic classification. Comput. J. 16: 254–261.CrossRefGoogle Scholar
  7. Brokaw, N. and R. T. Busing. 2000. Niche versus chance in tree diversity in forest gaps. TREE 15: 183–188.PubMedGoogle Scholar
  8. Bunge, M. 1969. Metaphysics, epistemology and methodology of levels. In: L. L. Whyte, A. G. Wilson and D. Wilson (eds.), Hierarchic Structures, American Elsevier, New York. pp. 17–28.Google Scholar
  9. Critchley, C. N. R. 2000. Ecological assessment of plant communities by reference to species traits and habitat preferences. Biodiversity and Conservation 9: 87–100.CrossRefGoogle Scholar
  10. Dale, M. B. 1976. Hierarchy and level: prolegomena to a cladistic classification Tech. Memo. 1, CSIRO Division of Tropical Crops and Pastures, St. Lucia, Brisbane.Google Scholar
  11. Dale, M.B. 1999. The dynamics of diversity: mixed strategy systems. Coenoses 13: 105–113.Google Scholar
  12. Dale, M. B. and D.J. Anderson. 1973. Inosculate analysis of vegetation data. Austral. J. Bot. 21: 253–276.CrossRefGoogle Scholar
  13. Dale, M. B. and M. M. Barson. 1989. On the use of grammars in vegetation science. Vegetatio 81: 79–94.CrossRefGoogle Scholar
  14. Dale, M. B. and P. Hogeweg. 1998. The dynamics of diversity: a cellular automaton approach. Coenoses 13: 3–15.Google Scholar
  15. Dale, M. B. and D. Walker. 1970. Information analysis of pollen diagrams. Pollen et Spores 2: 21–37.Google Scholar
  16. Diday, E. 1988. The symbolic approach in clustering and related methods of data analysis: the basic choices. In: H. H. Bock (ed.), Classification and Related Methods of Data Analysis, North Holland, Amsterdam. pp. 673–683.Google Scholar
  17. Edgoose, T and L. Allison. 1999. MML Markov classification of sequential data. Statistics and Computing 9: 269–278.CrossRefGoogle Scholar
  18. Edwards, R. T. and D. Dowe. 1998. Single factor analysis in MML mixture modelling. Lecture Notes in Art. Intell 1394 Springer, pp. 96–109.Google Scholar
  19. Gatsuk, L. E., O. V. Smirnova, L. I. Vorontzova, L. B. Zaugolnova and L. Zhukova. 1980. Age states of plants of various growth forms: a review. J. Ecol. 68: 675–696.CrossRefGoogle Scholar
  20. Hilderman, R. J. and H. J. Hamilton. 1999. Heuristics for ranking the interestingness of discovered knowledge. Proc. 3rd Pacific-Asia Conf. Knowledge Discovery PKDD’99, Beijing, Springer Verlag Berlin. pp. 204–209.Google Scholar
  21. Huisman, J., H. Olff, and L. F. M. Fresco. 1993. A hierarchical set of models for species response analysis. J. Vegetation Science 4: 37–46.CrossRefGoogle Scholar
  22. Kodratoff, Y. 1986. Leçons d’apprentissage symbolique, Cepaduesed., Toulouse.Google Scholar
  23. Kullback, S. and R. A. Leibler. 1951. On information and sufficiency. Ann. Math. Statist. 22: 79–86.CrossRefGoogle Scholar
  24. Legendre, P. and E. Gallagher. 2000. Ecologically meaningful transformations for ordination biplots of species data. Ecology (submitted).Google Scholar
  25. Mackay, D. M. 1969. Recognition and action. In: S. Watanabe (ed.), Methodologies of Pattern Recognition. Academic Press, London. pp. 409–416.CrossRefGoogle Scholar
  26. Pazzani, M. J. and D. Kibler. 1992. The utility of knowledge in inductive learning. Machine Learning 9: 57–94.Google Scholar
  27. Quinlan, R. and R. L. Rivest. 1989. Inferring decision trees using the Minimum Description Length Principle. Information and Computation 80: 227–248.CrossRefGoogle Scholar
  28. Rissanen, J. (1995) Stochastic complexity in learning. In: P. Vitányi (ed.), Computational Learning Theory, Lecture Notes in Computer Science, 904. Springer Verlag, Berlin, pp. 196–201.CrossRefGoogle Scholar
  29. Stevens, W. L. 1937. Significance of grouping. Ann. Eug. Lond. 8: 57–69.CrossRefGoogle Scholar
  30. Wallace, C. S. 1995 Multiple factor analysis by MML estimation Tech Rep. 95/218, Dept. Computer Science, Monash University, Australia.Google Scholar
  31. Wallace C. S. 1998. Intrinsic classification of spatially-correlated data. Comput. J. 41: 602–611.CrossRefGoogle Scholar
  32. Wallace, C. S. and D. M. Boulton. 1968. An information measure for classification. Comput. J. 11: 185–195.CrossRefGoogle Scholar
  33. Wallace, C. S. and D. L. Dowe. 2000. MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. Statistics and Computing 10: 73–83.CrossRefGoogle Scholar
  34. Watanabe, S. 1969. Knowing and Guessing. J. Wiley, New York.Google Scholar
  35. Wildi, O. and M. Schütz. 2000. Reconstruction of a long-term recovery process from pasture to forest. Community Ecology 1: 25–32.CrossRefGoogle Scholar
  36. Williams, W. T. and M. B. Dale. 1962. Partitioned correlation matrices for heterogenous quantitative data. Nature 196: 502.Google Scholar
  37. Williams, W. T., J. M. Lambert, and G. N. Lance. 1966. Multivariate methods in plant ecology. V. Similarity analysis and information analysis. J. Ecol. 54: 427–446.CrossRefGoogle Scholar
  38. Williams, W. T., G. N. Lance, L. J. Webb, J. G. Tracey and M. B. Dale. 1969. Studies in the numerical analysis of complex rain-forest communities. III. The analysis of successional change. J. Ecol. 57: 513–535.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2000

Authors and Affiliations

  1. 1.Australian School of Environmental StudiesGriffith UniversityNathanAustralia

Personalised recommendations