, Volume 81, Issue 1–2, pp 95–106 | Cite as

A new numerical solution to traditional phytosociological tabular classification

  • Otto Wildi


Often, manually and numerically derived phytosociological classifications yield different results. Hitherto, a twostep procedure has been suggested in which numerical analysis of the data is followed by the revision of the resulting table (c.f. van der Maarel 1982). In this paper a new methodology is presented which makes manual refinements superfluous. Objectives are derived from phytosociological paradigms and conclusions drawn for the analytical process. The problems to be solved are: data transformation, detection of outliers, selection of clustering methods, checking within-group diversity, analysis of the resulting group structure, rearrangement of relevés and species within the groups, and finally the selection of differential species. The method has been derived using the well known example of Ellenberg (Mueller-Dombois & Ellenberg 1974). The results almost perfectly reproduce the intuitively widely accepted manual refinements in structure and presentation. A test with plant sociological data from Swiss forests (Ellenberg & Klötzli 1972) proves that the method can also classify complex gradient- and group systems and that the numerical result matches Landolt's (1977) system of indicator values. Since the solutions can be exactly reproduced, it is no longer necessary to combine numerical analysis with additional editing.


Discriminant analysis Forest vegetation Gradient Indicator value Multivariate analysis Numerical syntaxonomy Outlier Switzerland Tabular sorting 


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  1. Braun-Blanquet, J. 1964. Pflanzensoziologie. Grundzüge der Vegetationskunde. (3rd ed.). Springer, Wien.Google Scholar
  2. Ellenberg, H. & Klötzli, F. 1972. Waldgesellschaften und Waldstandorte der Schweiz. Mitt. Schweiz. Anst. Forstl. Vers. w. 48: 589–930.Google Scholar
  3. Feoli, E. & Orlóci, L. 1979. Analysis of concentration and detection of underlying factors in structured tables. Vegetatio 40: 49–54.Google Scholar
  4. Gauch, H. G. 1982. Multivariate analysis in community ecology. Cambridge University. Press, Cambridge.Google Scholar
  5. Grabherr, G. 1985. Numerische Klassifikation und Ordination in der Alpinen Vegetationsökologie als Beitrag zur Verknüpfung moderner ‘Computermethoden’ mit der pflanzensoziologischen Tradition. Tuexenia 5: 181–190.Google Scholar
  6. Hill, M. O. 1974. Correspondence analysis: A neglected multivariate method. Appl. Statist. 23: 340–354.Google Scholar
  7. Hill, M. O. 1979a. TWINSPAN — a FORTRAN program for arranging multivariate data in an ordered two way table by classification of individuals and attributes. Cornell University, Ithaca.Google Scholar
  8. Hill, M. O. 1979b. DECORANA — a FORTRAN program for detrended correspondence analysis and reciprocal averaging. Cornell University, Ithaca.Google Scholar
  9. Hill, M. O. & Gauch, H. G. 1980. Detrended correspondence analysis, an improved ordination technique, Vegetatio 42: 47–58.Google Scholar
  10. Jancey, R. C. 1979. Species ordering on a variance criterion. Vegetatio 39: 59–63.Google Scholar
  11. Kenkel, N. & Orlóci, L. 1986. Applying metric and nonmetric multidimensional scaling to ecological studies: some new results. Ecology 67: 919–928.Google Scholar
  12. Kuhn, N. 1983. VEGTAB, ein Computer-Programm als Hilfe zur tabellarischen Vegetationsgliederung. Tuexenia 3: 499–522.Google Scholar
  13. Lagonegro, M. 1984. SPAGHET: A coenocline simulator useful to calibrate software detectors. Studia Geobot 4: 63–99.Google Scholar
  14. Landolt, E. 1977. Oekologische Zeigerwerte zur Schweizer Flora. Veröff. Geobot. Inst. ETH 64.Google Scholar
  15. Legendre, L. & Legendre, P. 1979. Ecologie numérique. Masson, Paris.Google Scholar
  16. Louppen, J. M. W. & van der Maarel, E. 1979. CLUSLA: A computer program for the clustering of large phytosociological data sets. Vegetatio 40: 107–114.Google Scholar
  17. Minchin, P. R. 1987. An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio 69: 89–107.Google Scholar
  18. Mueller-Dombois, D. & Ellenberg, E. 1974. Aims and methods of vegetation ecology. John Wiley Sons, New York.Google Scholar
  19. Orlóci, L. 1978. Multivariate analysis in vegetation research. (2nd ed.). Junk, The Hague.Google Scholar
  20. Poore, M. E. D. 1955. The use of phytosociological methods in ecological investigations. I–III. J. Ecol. 43: 226–244, 245–269, 606–651.Google Scholar
  21. Ropma, J., Mucina, L., van Tongeren, O. & van der Maarel, E. 1983. On the determination of optimal levels in phytosociological classification. Vegetatio 52: 65–75.Google Scholar
  22. van der Maarel, E. 1979. Transformation of cover-abundance values in phytosociology and its effects on community similarity. Vegetatio 39: 97–114.Google Scholar
  23. van der Maarel, E. 1982. On the manipulation and editing of phytosociological and ecological data. Vegetatio 50: 71–76.Google Scholar
  24. van der Maarel, E., Janssen, J. G. M. & Louppen, J. M. W. 1978. TABORD, a program for structuring phytosociological tables. Vegetatio 38: 143–156.Google Scholar
  25. Wildi, O. 1986. Analyse vegetationskundlicher Daten. Theorie und Einsatz statistischer Methoden. Veröff. Geobot. Inst. ETH 90.Google Scholar
  26. Wildi, O. Orlóci, L. 1983. Management and multivariate analysis of vegetation data. (2nd rev. ed.). Eidg. Anst. forstl. Versuchswes. Ber. 215.Google Scholar

Copyright information

© Kluwer Academic Publishers 1989

Authors and Affiliations

  • Otto Wildi
    • 1
  1. 1.Swiss Federal Institute of Forestry ResearchBirmensdorfSwitzerland

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