Essential Site Maintenance: Authorea-powered sites will be updated circa 15:00-17:00 Eastern on Tuesday 5 November.
There should be no interruption to normal services, but please contact us at [email protected] in case you face any issues.

loading page

On estimating the shape and dynamics of phenotypic distributions in ecology and evolution
  • +11
  • Brian Maitner,
  • Aud Halbritter,
  • Richard Telford,
  • Tanya Strydom,
  • Julia Chacon-Labella,
  • Christine Lamanna,
  • Lindsey Sloat,
  • Andrew Kerkhoff,
  • Julie Messier,
  • Nick Rasmussen,
  • Francesco Pomati,
  • Ewa Merz,
  • Vigdis Vandvik,
  • Brian Enquist
Brian Maitner
University of Arizona

Corresponding Author:[email protected]

Author Profile
Aud Halbritter
Universitetet i Bergen Det Matematisk-naturvitenskapelige Fakultet
Author Profile
Richard Telford
Universitetet i Bergen Det Matematisk-naturvitenskapelige Fakultet
Author Profile
Tanya Strydom
University of Montreal
Author Profile
Julia Chacon-Labella
Universidad Rey Juan Carlos Escuela Superior de Ciencias Experimentales y Tecnologia
Author Profile
Christine Lamanna
World Agroforestry Centre
Author Profile
Lindsey Sloat
Colorado State University
Author Profile
Andrew Kerkhoff
Kenyon College
Author Profile
Julie Messier
Universite de Sherbrooke
Author Profile
Nick Rasmussen
California Department of Water Resources
Author Profile
Francesco Pomati
EAWAG
Author Profile
Ewa Merz
Swiss Federal Institute of Aquatic Science and Technology (Eawag)
Author Profile
Vigdis Vandvik
Universitetet i Bergen Det Matematisk-naturvitenskapelige Fakultet
Author Profile
Brian Enquist
University of Arizona
Author Profile

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.