A new algorithm for the determination of differential taxa

How can we determine differential taxa in a vegetation data set? The new algorithm presented here uses an intuitive fidelity threshold based on relative constancy differences. It is tested on a simulated and a real data set. The results of the proposed algorithm are discussed in comparison with other methods used for the determination of differential taxa. The new algorithm defines each taxon in each group of relevEs as: (1) positively differentiating, (2) positively-negatively differentiating, (3) negatively differentiating, or (4) non-differentiating. Each taxon in a data set may be: (1) positively, positively-negatively or negatively differentiating for each group in the data set, (2) differentiating for some groups and non-differentiating for the remaining groups, or (3) non-differentiating for all groups in the data set. The new algorithm finds the relevE groups that are positively differentiated against other groups that are negatively differentiated. It reveals differentiating structures in the data set and thus makes quantification of the relations among and between different syntaxonomic ranks conceivable. As it distinguishes between different types of differential taxa, it might improve standards of typification in vegetation classification.