Diversity patterns of vegetation systems from the perspective of similarity theory

Abstract

In this article, I suggest a simple method, based on similarity theory that can be used to generate diversity patterns of vegetation systems at different hierarchical levels of their description. It is a very flexible method that measures the diversity according to hierarchical classifications of vegetation units (VUs) sampled from the vegetation system under study. The VUs may be either individual plants or vegetation relevès (preferably of the Braun Blanquet approach). It follows that the diversity measures of a vegetation system can be “individual plant based diversity measures” or “plant community based diversity measures”. The two kinds of measures of vegetation diversity are complementary and the choice to calculate both or a single one of them depends on the aim of the study. The method consists in a procedure that computes the similarity between the VUs on the basis of a set of characters that can be defined from single different disciplines (taxonomy, evolution, chemistry, chorology, etc.) or combinations of them. The VUs are hierarchically classified by any logical hierarchical classification method (a dendrogram can be used to suggest the hierarchical classification when there are no other logical alternatives) and the diversity is calculated for each hierarchical level by using the frequency of the VUs in the classes. The diversity is calculated in two ways, the crisp way and the fuzzy way. In the crisp way, the within similarity of the classes is assumed to be equal to 1 and the between similarity is assumed to be equal to 0. In the fuzzy way, the crisp diversity is corrected (fuzzified) according to the similarity between the classes based on the set or subsets of the characters that have been used to define the classes (internal characters) or on the basis of other sets of characters (external characters). In both cases, the method produces hierarchical diversity profiles that can be used to compare the states of the vegetation systems in time and/or space. I show an example of the application of the method with a hypothetical data set.