Community Ecology

, Volume 3, Issue 2, pp 147–157 | Cite as

Probabilistic classification and its application to vegetation science

  • D. W. GoodallEmail author
Open Access


Probabilistic classification offers various advantages in its application to vegetation studies: it can use data in the form of ordered as well as quantitative values; it can use a range of values for each attribute (species) in each relevé or group of relevés; it can use incomplete data sets; it takes account of all species, rather than only characteristic species; and it enables a null hypothesis of random distribution of species among relevés to be tested.

The procedure is here explained in some detail, and its application is illustrated, first with a classical data set from the Alps, and second with an extract from the extensive Netherlands national data base.

It is shown that the presence or absence of species is often more informative about the relationships between relevés than the quantities in which they are present. The results do not support the concept of discrete and uniform vegetation units, but rather of vegetation composition varying around centres of concentration.


Festucion valesiacae Hypothesis testing Nardo-Galion Ordinal data Phytosociology Vegetation classification 



I am beholden to the ecologists at Wageningen in The Netherlands, particularly Dr. Stephan Hennekens, for making available to me a sample from their enormous data bank for Netherlands vegetation.


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© Akadémiai Kiadó, Budapest 2002

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.Centre for Ecosystem ManagementEdith Cowan UniversityJoondalupAustralia

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