Functional group classification using consensus clustering

Functional diversity is a fundamental aspect of community structure and composition, reflecting diversity and redundancy in ecological niches, functional roles, and environmental responses among species within a community. Despite its growing importance for quantifying ecosystem-level biodiversity, existing functional diversity metrics remain difficult to calculate and interpret, hindering their adoption and application beyond the scientific realm. One potential solution to this problem is to categorize species into functional groups based on their traits, which provides a simple, intuitive categorization of functional diversity that allows for the application of traditional species-based metrics. The functional-group approach, however, has several challenges that have limited its adoption, namely, the difficulty in identifying robust functional clusters, which can vary substantially due to trait variability, measurement error, and trait correlation. Here, to address these challenges, we present a multi-step consensus clustering method that integrates trait uncertainty and correlation into the classification of species into functional groups. Our approach proceeds in four main steps: (1) (re)sample trait data from an underlying distribution or with measurement error, (2) fit a Gaussian Mixture Model (to account for correlation) to each resample, (3) build a consensus matrix quantifying how often species pairs are grouped together across the noisy trait sample, and (4) apply traditional hierarchical clustering to this matrix and select the final groups. As a case study of this approach, we apply this method to a global dataset of 47,828 tree species using 18 traits, identifying 42 functional groups with distinct trait patterns and varying degrees of stability. We show how the resulting groups reflect underlying ecological trade-offs and phylogenetic structure, and we demonstrate how traditional diversity metrics (richness and Simpson's Index) can be applied to these functional groups to provide intuitive measures of functional group richness and functional redundancy. Collectively, this framework presents a scalable, interpretable approach for quantifying functional groups that embraces trait correlation and trait uncertainty, allowing for repeatable and intuitive quantification of functional biodiversity that can aid its adoption in biodiversity assessments by conservation and restoration organisations.