A Unified and Predictive Measure of Functional Diversity

Abstract

Despite the critical role of functional diversity (FD) in understanding ecological systems and processes, its robust quantification remains a significant challenge. A long-held view in the field is that it is not possible to capture its three facets -- functional richness, functional divergence, and functional evenness -- in a single index. This perspective has prompted recent proposals for FD measurement to use three separate indices, one for each aspect. Here, we challenge this paradigm by demonstrating that the probability-weighted Vendi Score (pVS), first introduced by Friedman and Dieng (2023), can serve as a powerful functional diversity index that can capture its three facets. We adapt pVS to functional ecology by defining it as the exponential of the Rényi entropy of the eigenvalues of the abundance-weighted trait similarity matrix. This formulation allows pVS to be applicable at any biological level. It can be defined at the species level, at which most existing FD metrics are defined, and at the individual level to naturally incorporate intraspecific trait variation (ITV) when detailed data are available. We theoretically and empirically demonstrate the robustness of pVS. We first mathematically prove it satisfies several essential desiderata for FD metrics, including invariance to functional redundancy, set monotonicity, distance monotonicity, and concavity. We then show that pVS consistently exhibits the expected ground-truth behavior on simulated ecosystem scenarios under which many FD metrics fail. By integrating abundances and trait similarities within a single, theoretically sound framework, pVS provides a generally applicable index for ecology.