Functional diversity metrics can perform well with highly incomplete data sets

作者：Kerry Stewart, Carlos P. Carmona, Christopher F. Clements, Chris Venditti, Joseph A. Tobias, Manuela González‐Suárez · 发表于：Methods in Ecology and Evolution · 年份：2023 · DOI：10.1111/2041-210x.14202 · 被引用次数：33 · 研究领域：Ecology and Vegetation Dynamics Studies、Species Distribution and Climate Change、Wildlife Ecology and Conservation

Abstract Characterising changes in functional diversity at large spatial scales provides insight into the impact of human activity on ecosystem structure and function. However, the approach is often based on trait data sets that are incomplete and unrepresentative, with uncertain impacts on functional diversity estimates. To address this knowledge gap, we simulated random and biased removal of data from three empirical trait data sets: an avian data set (9579 species), a plant data set (2185 species) and a crocodilian data set (25 species). For these data sets, 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). Without imputation, estimates of functional diversity (richness and divergence) for birds and plants were robust when 20%–70% of species had missing data for four out of 11 and two out of six continuous traits, respectively, depending on the severity of bias and method used. However, when missing traits were imputed, functional diversity metrics consistently remained representative of the true value when 70% of bird species were missing data for four out of 11 traits and when 50% of plant species were missing data for two out of six traits. Trait pro...