Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

作者：Brian Maitner, Aud H. Halbritter, Richard J. Telford, Tanya Strydom, Julia Chacón‐Labella, Christine Lamanna, Lindsey Sloat, Andrew J. Kerkhoff, Julie Messier, Nick L. Rasmussen, Francesco Pomati, Ewa Merz, Vigdis Vandvik, Brian J. Enquist · 发表于：Methods in Ecology and Evolution · 年份：2023 · DOI：10.1111/2041-210x.14160 · 被引用次数：37 · 研究领域：Ecology and Vegetation Dynamics Studies、Species Distribution and Climate Change、Wildlife Ecology and Conservation

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill , allows the user to specify hierarchical struc...