A user's guide to functional diversity indices

Abstract

Abstract. Functional diversity is the diversity of species traits in ecosystems. This concept is increasingly used in ecological research. Yet its formal definition and measurements are currently under discussion. As the overall behavior and consistency of functional diversity indices have never been described so far, the novice user risks choosing an inaccurate index or a set of redundant indices to represent functional diversity.In our study we closely examine functional diversity indices to clarify their accuracy, consistency, and independency. Following current theory, we categorize them into functional richness, evenness, or divergence indices. We considered existing indices as well as new indices developed in this study. The new indices aimed at remedying the weaknesses of currently used indices (e.g., by taking into account intraspecific variability). Using virtual datasets, we first test whether indices respond to community changes as expected from their category, and second, whether the indices within each category are consistent and independent of indices from other categories. We also test the accuracy of methods proposed for the use of categorical traits.Most classical functional richness indices either failed to describe functional richness or were correlated with functional divergence indices. We therefore recommend using the new functional richness indices which consider intraspecific variability and thus empty space in the functional niche space. In contrast, most functional evenness and divergence indices performed well with respect to all proposed tests. For categorical variables, we do not recommend blending discrete and real-valued traits (except for indices based on distance measures) since functional evenness and divergence have no transposable meaning for discrete traits. Nonetheless, species diversity indices can be applied to categorical traits (using trait levels instead of species) in order to describe functional richness and equitability.

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“…However, in the present study, differences in approach or computational details proved not to influence outcomes of the relationships between mixed-litter dissimilarity and decomposition rate. That is, none of a range of advocated metrics (Walker et al 1999, Petchey and Gaston 2002, Podani and Schmera 2006, Schleuter et al 2010) that we assessed were significantly related to litter decomposition rate, irrespective of whether decomposition was mediated by microbes alone (decomposition in fine-mesh litterbags) or by macroinvertebrates and microbes in concert (data from coarse-mesh bags). …”

Functional traits in agriculture: agrobiodiversity and ecosystem services

“…A guide should be used to select traits and determine standard measurement [75,76]. metrics of functional trait composition can then be calculated (e.g., mean values or diversity, depending on the service of interest [77]). …”

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