Measuring overall functional diversity by aggregating its multiple facets: Functional richness, biomass evenness, trait evenness and dispersion - Laurie Anne Wojcik; Ursula Gaedke; Ellen vanVelzen; Toni Klauschies (Methods in Ecology and Evolution)
Abstract Human activities induce environmental changes, which can affect individuals' traits thereby leading to changes in functional diversity and finally in ecosystem functioning. Measuring functional diversity is thus of utmost importance to understand the consequences of such activities on ecosystem functioning. Functional diversity is composed of several facets, but these facets are almost always measured individually, and we lack a common metric integrating the multifaceted nature of functional diversity. Consequently, we developed an index K defined as the geometric mean of four independent facets: functional richness (the classic measure of the coverage of the trait axis), biomass evenness and trait evenness (quantifying how evenly distributed the biomass and traits are among species and within the feasible trait range, respectively) and dispersion (quantifying the spread around the biomass‐weighted mean trait). K and each of its underlying facet take values between 0 and 1, and they assume the uniform distribution to yield maximal diversity. We compared K to other, more classic metrics measuring fewer facets of functional diversity by calculating all these indices for randomly and non‐randomly generated communities. We showed that K overcomes several limitations of other indices (e.g. lack of accuracy, not computable for simple communities, unclear ecological interpretation) and is strongly correlated with ecosystem functions in simulated predator–prey communities. In addition, decomposing K into its underlying facets revealed that ecosystem functions can be driven by different facets of K on different trophic levels. The strength of our index K lies in being the only index that measures accurately the overall functional diversity by combining several facets and providing the option to decompose K into them. This notably yields mechanistic insights about which facets are more important for driving changes in functional diversity and ecosystem functioning.