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

Peer review status:UNDER REVIEW

20 May 2021Submitted to Ecology Letters
25 May 2021Assigned to Editor
25 May 2021Submission Checks Completed
25 May 2021Reviewer(s) Assigned