Abstract
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. Therefore, it is not always adequate for estimating beta diversity. Next, I propose an alternative approach for calculating beta diversity in which variance is substituted by a weighted measure of concentration (i.e., an inverse measure of evenness). The relationship between this new measure of beta diversity and so‐called multiple‐site dissimilarity measures is also discussed.
Highlights
The concept of beta diversity dates back to the work of Whittaker (1960), which coined this term to define the amount of variation in species composition among sampling units
Given a set of N plots, Whittaker (1960) proposed to summarize beta diversity as the ratio of two inventory diversities measured at different scales, such that β = γ/α, where α is the average diversity of the N plots and γ is the total diversity of the pooled set of plots
I introduced a method for calculating the beta diversity of a community composition table, which preserves the relative dispersion of abundances within species vectors
Summary
The concept of beta diversity dates back to the work of Whittaker (1960), which coined this term to define the amount of variation in species composition among sampling units (or communities, assemblages, plots, relevés, sites, quadrats, etc.). KEYWORDS community composition matrix, dissimilarity measures, evenness, species abundances, total sum of squares, variance decomposition
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