Probabilistic classification and its application to vegetation science

Abstract

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.

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Acknowledgements

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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Correspondence to D. W. Goodall.

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Goodall, D.W. Probabilistic classification and its application to vegetation science. COMMUNITY ECOLOGY 3, 147–157 (2002). https://doi.org/10.1556/ComEc.3.2002.2.2

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Keywords

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