Community Ecology

, Volume 1, Issue 1, pp 33–41 | Cite as

Additive trees in the analysis of community data

  • J. PodaniEmail author
  • P. Csontos
  • J. Tamás


The paper advocates a more extensive use of additive trees in community ecology. When the distance/dissimilarity coefficient is selected carefully, these trees can illuminate structural aspects that are not obvious otherwise. In particular, starting from squared distances based on presence/absence data, the resulting trees approximate relationships in species richness, a feature not available through other graphical techniques. The construction of additive trees is illustrated by three actual examples, representing different circumstances in the analysis of grassland community data.


Classification Dendrograms Four-point metrics Grasslands Neighbor joining Succession Syntaxonomy Ultrametrics 



Neighbor Joining


Principal Components Analysis


Unweighted Pair Group Method Using Arithmetic Averages.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Buneman, P. 1971. The recovery of trees from measures of dissimilarity. In: F.R. Hodson, D.G. Kendall and P. Tautu (eds). Mathematics in the Archaeological and Historical Sciences. Edinburgh Univ. Press, Edinburgh, pp. 387–395.Google Scholar
  2. Carleton, T.J. 1980. Non-centered component analysis of vegetation data: a comparison of orthogonal and oblique rotation. Vegetatio 42:59–66.CrossRefGoogle Scholar
  3. Carroll, J. D. and J. J. Chang. 1976. Spatial, non-spatial and hybrid models for scaling. Psychometrika 41:439–463.CrossRefGoogle Scholar
  4. Cavalli-Sforza, L. L., A. Piazza, P. Menozzi and J. L. Mountain. 1988. Reconstruction of human evolution: bringing together genetic, archaeological and linguistic data. Proc. Natl. Acad. Sci USA 85:6002–6006.CrossRefGoogle Scholar
  5. Corter, J.E. 1982. ADDTREE/P: a PASCAL program for fitting additive trees based on Sattath and Tversky’s ADDTREE algorithm. Behav. Res. Meth. Instrument. 14: 353–354.CrossRefGoogle Scholar
  6. Cunningham, J. P. 1978. Free trees and bidirectional trees as representations of psychological distance. J. Math. Psychol. 17:165–188.CrossRefGoogle Scholar
  7. Dale, M. B. 1989. Mutational and nonmutational similarity measures. Coenoses 3:121–133.Google Scholar
  8. Dale, M. B. 2000. On plexus representations of dissimilarities. Community Ecology 1.CrossRefGoogle Scholar
  9. Dale, M. B., M. Beatrice and R. Venanzoni. 1986. A comparison of some methods of selecting species in vegetation analysis. Coenoses 1:35–52.Google Scholar
  10. Day, W. H. E. and H. Edelsbrunner. 1984. Efficient algorithms for agglomerative hierarchical clustering methods. J. Classif. 1:7–24.CrossRefGoogle Scholar
  11. de Soete, G. 1983. A least squares algorithm for fitting additive trees to proximity data. Pychometrika 48: 621–626.CrossRefGoogle Scholar
  12. de Soete, G. 1988. Tree representations of proximity data by least squares methods. In: H. H. Bock (ed.), Classification and Related Methods of Data Analysis, North Holland, Amsterdam, pp. 147–156.Google Scholar
  13. Digby, P. G. N. and R. A. Kempten. 1987. Multivariate Analysis of Ecological Communities. Chapman and Hall, London.CrossRefGoogle Scholar
  14. Gascuel, O. 1994. A note on Sattath and Tversky’s, Saitou and Nei’s, and Studier and Keppler’s algorithms for inferring phylogenies from evolutionary distances. Mol. Biol. Evol. 11:961–963.PubMedGoogle Scholar
  15. Goodall, D. W. 1953. Objective methods for the classification of vegetation I. The use of positive interspecific correlation. Aust. J. Bot. 1:39–63.Google Scholar
  16. Lance, G. N. and W. T. Williams. 1967. A general theory of classificatory sorting strategies. I. Hierarchical systems. Computer J. 9:373–380.Google Scholar
  17. Legendre, P. 1986. Reconstructing biogeographic history using phylogenetic-tree analysis of community structure. Syst. Zool. 35:68–80.CrossRefGoogle Scholar
  18. Michalski, R. S., I. Bratko and M. Kubat (eds). 1998. Machine learning and data mining: Methods and Applications. Wiley, New York.Google Scholar
  19. Nei, M. 1996. Phylogenetic analysis in molecular evolutionary genetics. Annu. Rev. Genet. 30:371–403.CrossRefGoogle Scholar
  20. Orlóci, L. 1967. An agglomerative method for classification of plant communities. J. Ecol. 55:193–205CrossRefGoogle Scholar
  21. Page, R. D. M. and E. C. Holmes. 1998. Molecular Evolution. A Phylogenetic Approach. Blackwell, Oxford.Google Scholar
  22. Patrinos, A. N. and S. L. Hakimi. 1972. The distance matrix of a graph and its tree realization. Q. Appl. Math. 30:255–269.CrossRefGoogle Scholar
  23. Podani, J. 1985. Syntaxonomic congruence in a small-scale vegetation survey. Abstracta Botanica 9: 99–128.Google Scholar
  24. Podani, J. 1994. Multivariate Data Analysis in Ecology and Systematics. SPB Publishing, The Hague.Google Scholar
  25. Podani, J. 1997. SYN-TAX 5.1: A new version for PC and Macintosh computers. Coenoses 12:149–152.Google Scholar
  26. Podani, J. 1998. Numerikus cönológiai vizsgálatok a Sas-hegy (Budai-hg.) dolomitsziklagyepjeiben. (A complex numerical analysis of dolomite rock grasslands of the Sas-hegy Nature Reserve, Budapest, Hungary. In Hungarian with English summary) In: P. Csontos (ed.), Sziklagyepek szünhotanikai kutatása. Scientia, Budapest, pp. 211–229.Google Scholar
  27. Podani, J. 2000. Simulation of random dendrograms: some comments. J. Classif. 17 (in press).Google Scholar
  28. Rosen, D. E. 1978. Vicariant patterns and historical explanation in biogeography. Syst. Zool. 27:159–188.CrossRefGoogle Scholar
  29. Saitou, N. and M. Nei. 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4:406–425.PubMedPubMedCentralGoogle Scholar
  30. Sattath, S. and Tversky, A. 1977. Additive similarity trees. Psychometrika 42:319–344.CrossRefGoogle Scholar
  31. Shepard, R. N. 1980. Multidimensional scaling, tree-fitting, and clustering. Science 210:390–398.CrossRefGoogle Scholar
  32. Simon, T. 1992. A magyarországi edényes flóra határozója. Harasztok - Virágos növények. Tankönyvkiadó, Budapest.Google Scholar
  33. Sneath, P.H.A. and Sokal, R. R. 1973. Numerical Taxonomy. Freeman, San Francisco.Google Scholar
  34. Swofford, D. L. and G. J. Olsen. 1990. Phylogeny reconstruction. In: D. M. Hillis and C. Moritz (eds.), Molecular Systematics. Sinauer, Sunderland, Mass. pp. 411–501.Google Scholar
  35. Tamás, J. and P. Csontos 1998. A növényzet tűz utáni regenerálódása dolomitra telepített feketefenyvesek helyén. (Early regeneration of dolomite vegetation after burning of Pinus nigra plantations. In Hungarian with English summary.) In: P. Csontos (ed.), Sziklagyepek szünhotanikai kutatása. Scientia, Budapest, pp. 231–264.Google Scholar
  36. Török, K. and B. Zólyomi 1998. A Kárpát-medence öt sziklagyeptársulásának szüntaxonómiai revíziója. (Syntaxonomic revision of five rocky grassland communities of the Carpathian Basin. In Hungarian with English summary.) In: P. Csontos (ed.), Sziklagyepek szünhotanikai kutatása. Scientia, Budapest, pp. 109–132.Google Scholar
  37. Westphal, C. and T. Blaxton. 1998. Data Mining Solutions. Wiley, New YorkGoogle Scholar
  38. Wildi, O. and M. Schütz. 2000. Reconstruction of a long-term recovery process from pasture to forest. Community Ecology 1.CrossRefGoogle Scholar
  39. Williams, W. T. and J. M. Lambert. 1959. Multivariate methods in plant ecology. I. Association-analysis in plant communities. J. Ecol. 47:83–101.CrossRefGoogle Scholar
  40. Zólyomi, B. 1958. Budapest ós környékének növénytakarója. In: M. Pécsi (ed.), Budapest természeti képe. Akadémiai Kiadó, Budapest, pp. 508–642.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2000

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Plant Taxonomy and EcologyEötvös UniversityBudapestHungary

Personalised recommendations