On estimating the shape and dynamics of phenotypic distributions in
ecology and evolution
Abstract
Estimating the distribution of phenotypes in populations and communities
is central to many questions in ecology and evolutionary biology. These
distributions can be characterized by their moments: the mean, variance,
skewness, and kurtosis. Typically, these moments are calculated using a
community-weighted approach (e.g. community-weighted mean) which ignores
intraspecific variation. As an alternative, bootstrapping approaches can
incorporate intraspecific variation to improve estimates, and also
quantify uncertainty in the estimate. Here, we compare the performance
of different approaches for estimating the moments of trait
distributions across a variety of sampling scenarios, taxa, and
datasets. We introduce the traitstrap R package to facilitate inferences
of trait distributions via bootstrapping. Our results suggest that
randomly sampling ~9 individuals per sampling unit and
species, focusing on covering all species in the community, and
analysing the data using nonparametric bootstrapping generally enables
reliable inference on trait distributions, including the central
moments, of communities.