A new parametric measure of functional dissimilarity: Bridging the gap between the Bray-Curtis dissimilarity and the Euclidean distance

Abstract

Community ecologists usually consider the Euclidean distance inappropriate to explore the multivariate structure of species abundance data. This is because the Euclidean distance may lead to the counterintuitive result for which two sample plots with no species in common may be more similar to each other than two plots that share the same species list. To overcome this paradoxical situation, the species abundances need to be normalized in some way. Among the many coefficients used by ecologists for the analysis of assemblage data, the Bray-Curtis dissimilarity is certainly the most commonly used. This measure entails normalization of species-wise differences between two plots by the total species abundance in both plots. By highlighting the relationship between the Bray-Curtis dissimilarity and the Euclidean distance, we propose a parametric dissimilarity measure that is appropriate for handling data on community composition. We also show how the new parametric measure can be generalized to the measurement of functional dissimilarity between two plots. A small dataset on the species functional turnover along a chronosequence on Alpine grasslands is used to illustrate the behavior of the proposed measure.