Most existing functional diversity indices focus on single specific facet of functional diversity. Although they help scrutinize the details of functional diversity from their own angles, they often present some limitations in estimating functional diversity from a broad perspective. Here we presented a functional extension and evenness (FEE) index that encloses two important aspects of functional diversity. This index is based on a straightforward notion that a community has high diversity when its species are distant from each other in trait space. We quantified the functional diversity by evaluating the overall extension of species traits and interspecific trait differences of a species assemblage in trait space. The concept of minimum spanning tree (MST) of points was adopted to obtain the essential distribution properties for a community in trait space. We integrated the total length of MST branches (extension) and the variation of branch lengths (evenness) into the FEE₀ metric, and then translated FEE₀ to a species-richness-independent FEE index using a null model approach. We assessed the properties of FEE and used multiple approaches to evaluate FEE’s performance. The results show that the FEE index performs well in quantifying functional diversity and presents the following desired properties: 1) it allows a fair comparison of functional diversity across different species richness; 2) it preserves the essence of facet-specific indices and overcomes some constraints that other facet-specific indices displayed; 3) it takes species pool into consideration; and 4) has the potential to distinguish underlying processes that govern species assemblies. With these attributes, we suggest that the FEE index is a promising metric to inform biodiversity conservation policy and management, especially in applications at large spatial or/and temporal scales.

The data include empirical cumulative distribution functions (eCDFs) used to derive the functional extension and evenness (FEE) index, the simulated species traits and abundances used in our analyses, and the FEE and other indices pre-calculated for the simulated communities.

The R code performs all analyses in our paper, including generations of eCDFs using null model approach, calculations of the FEE and other indices for the simulated communities, statistical analyses, and generating figures.

Refer to README.txt file.