Carlo Ricotta

Functional diversity is regarded as a key concept for understanding the link between ecosystem function and biodiversity. The different and ecologically well‐defined aspects of the concept are reflected by the so‐called functional components , for example, functional richness and divergence. Many authors proposed that components be distinguished according to the multivariate technique on which they rely, but more recent studies suggest that several multivariate techniques, providing different functional representations (such as dendrograms and ordinations) of the community can in fact express the same functional component. Here, we review the relevant literature and find that (1) general ecological acceptance of the field is hampered by ambiguous terminology and (2) our understanding of the role of multivariate techniques in defining components is unclear. To address these issues, we provide new definitions for the three basic functional diversity components namely functional richness, functional divergence and functional regularity. In addition, we present a classification of presence‐/absence‐based approaches suitable for quantifying these components. We focus exclusively on the binary case for its relative simplicity. We find illogical, as well as logical but unused combinations of components and representations; and reveal that components can be quantified almost independently from the functional representation of the community. Finally, theoretical and practical implications of the new classification are discussed.

In trait‐based ecology, phenotypic variation (PVar) is often quantified with measures expressing average differences between populations standardized in the range 0–1. However, these measures disregard the within‐population trait variability. In addition, some of them cannot be partitioned between populations. These aspects can either alter their interpretation or limit their applicability. To overcome these problems, we propose a new measure, the phenotypic dissimilarity (PhD) index, to quantify PVar between populations in scenarios of varying within‐population interindividual trait variability. PhD can also quantify within‐population PVar while accounting for intraindividual trait variability. Using simulated and real data, we show that using the PhD index becomes important when the within‐population trait variability is not negligible, as in all ecological studies. By accounting for within‐population trait variability, the PhD index does not overestimate PVar across an environmental gradient compared to other estimators. Traits inherently vary within species. Accounting for such variability is essential to understanding species’ phenotypic responses to environmental cues. The proposed PhD index will provide ecologists with a tool for quantifying PVar within species and compare it between species at different levels of biological organization. We provide an R function to calculate the PhD index.