This archive contains data and scripts supporting ‘Functional diversity metrics can perform well with highly incomplete datasets’.
We aimed to quantify changes in functional diversity due to missing and biased data in two types of diversity metrics: functional richness and functional divergence, each estimated with two methods, and to determine if data imputation methods reduce errors and biases in estimated metrics. We identified the degree of missingness and bias permissible when quantifying large-scale functional diversity.
We simulated random and biased removal of data from three empirical trait datasets: an avian dataset (9579 species, Tobias et al., 2021), a plant dataset (2185 species, Díaz al., 2022) and a crocodilian dataset (25 species, Griffith et al. 2022). For these datasets we assessed whether functional diversity metrics were robust to data incompleteness with and without using imputation to fill data gaps. We compared two metrics each calculated with two methods: functional richness (calculated with convex hulls and trait probabilities densities) and functional divergence (calculated with distance-based Rao and trait probability densities). 

This archive contains all scripts supporting these analyses, simulated incomplete datasets (both with and without imputed values), and results tables of functional diversity estimations for each simulated incomplete dataset.