The use of the evenness (E(λ)) of the eigenvalues of similarity matrices corresponding to different hierarchical levels of ecosystem classifications, is suggested to test correlation (or predictivity) between biological communities and environmental factors as one alternative of analysis of variance (parametric or non-parametric). The advantage over traditional methods is the fact that similarity matrices can be obtained from any kind of data (mixed and missing data) by indices such as those of Goodall and Gower. The significance of E(λ) is calculated by permutation techniques. One example of application of E(λ) is given by a data set describing plant community types (beech forests of the Italian peninsula).

In this article, we consider the meaning of the phytosociological relevé of the Braun-Blanquet's approach as a source of biodiversity matrices. We stress the importance of phytosociological relevé for the integrated vegetation studies dedicated to understand the vegetation system and its ecosystem role at different hierarchical levels. We review the state of the art of building databases of the Italian vegetation under the perspective to develop a knowledge database of Italian vegetation based on formal ontologies. This would constitute a node in the possible network of knowledge databases of European Union and of the world that is proposed within the International Association of Vegetation Science.

It is shown that a similarity theory (ST) formulated in the context of plant community reasearch can lead to methodological developments based on similarity functions. ST can explain and predict ecosystems states, discover links between the physical-chemical environment and the plant communities at different scales of gegeralization. ST is compared with fuzzy systems theory, which supports similar developments, and it conlcuded that fuzzy set theory could be considered as an extension of similarity theory