Ricerc@Sapienza

The amount of variation in species composition among sampling units or beta diversity has become a primary tool for connecting the spatial structure of species assemblages to ecological processes. Many different measures of beta diversity have been developed. Among them, the total variance in the community composition matrix has been proposed as a single-number estimate of beta diversity. In this study, I first show that this measure summarizes the compositional variation among sampling units after nonlinear transformation of species abundances.

In this paper, we examine some basic properties of the Bray-Curtis dissimilarity as compared with other
distance and dissimilarity functions applied to ecological abundance data. We argue that the ability of
every coefficient to measure species-level contributions is a fundamental requirement. By suggesting an
additive decomposition formula for the Bray-Curtis coefficient we derive a general formula of
dissimilarity, which includes the Canberra distance and the Bray-Curtis dissimilarity as special cases. A

In this paper, I propose a new evenness-based method for calculating plot-to-plot (dis)similarity coefficients. The method is very flexible, as (dis)similarity can be calculated for any kind of species abundance data (also including functional or phylogenetic differences between species), and can be easily generalized to multiple sites. To show how the proposed method works in practice, the behavior of two similarity coefficients based on Pielou's and Williams’ evenness is examined with simulated data representing an ideal ecological gradient.