A guide to between-community functional dissimilarity measures

Abstract

In the recent years a variety of indices have been proposed with the aim of quantifying functional dissimilarity between communities. These indices follow different approaches to account for between-species similarities in the calculation of community dissimilarity, yet they all have been proposed as straightforward tools.In this paper we reviewed the trait-based dissimilarity indices available in the literature, contrasted the approaches they follow, and evaluated their performance in terms of correlation with an underlying environmental gradient using individual-based community simulations with different gradient lengths. We tested how strongly dissimilarities calculated by different indices correlate with environmental distances. Using random forest models we tested the importance of gradient length, the choice of data type (abundance vs. presence/absence), the transformation of between-species similarities (linear vs. exponential), and the dissimilarity index in the predicting correlation value.We found that many indices behave very similarly and reach high correlation with environmental distances. There were only a few indices (e.g. Rao’s DQ, and representatives of the nearest neighbour approach) which performed regularly poorer than the others. By far the strongest determinant of correlation with environmental distance was the gradient length, followed by the data type. The dissimilarity index and the transformation method seemed not crucial decisions when correlation with an underlying gradient is to be maximized.Synthesis: We provide a framework of functional dissimilarity indices and discuss the approaches they follow. Although, these indices are formulated in different ways and follow different approaches, most of them perform similarly well. At the same time, sample properties (e.g. gradient length) determine the correlation between trait-based dissimilarity and environmental distance more fundamentally.

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

“…Many authors have proposed a set of guidelines to help navigate the multitude of existing dissimilarity coefficients for the analysis of ecological data (e.g. Gower and Legendre 1986;Podani 2000;Legendre and De Cáceres 2013;Lengyel and Botta-Dukát 2021). However, selecting an appropriate question-specific coefficient is not always a simple operation.…”