Apr 5, 2016

Bias in community-weighted mean analysis of plant functional traits and species indicator values

BioRxiv : the Preprint Server for Biology
David Zeleny

Abstract

One way to analyze the relationship between species attributes and sample attributes via the matrix of species composition is to calculate the community-weighted mean of species attributes (CWM) and relate it to sample attributes by correlation, regression or ANOVA. This weighted-mean approach is frequently used by vegetation ecologists to relate species attributes like plant functional traits or Ellenberg-like species indicator values to sample attributes like measured environmental variables, biotic properties, species richness or sample scores in ordination analysis. The problem with the weighted-mean approach is that, in certain cases, it yields biased results in terms of both effect size and P-values, and this bias is contingent upon the beta diversity of the species composition data. The reason is that CWM values calculated from samples of communities sharing some species are not independent of each other. This influences the number of effective degrees of freedom, which is usually lower than the actual number of samples, and the difference further increases with decreasing beta diversity of the data set. The discrepancy between the number of effective degrees of freedom and the number of samples in analysis turns into bi...Continue Reading

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Mentioned in this Paper

Neuro-Oncological Ventral Antigen 2
Study
Laboratory Procedures
Extracellular Matrix
Environment
Diagnostic Procedure
Species
Analysis
NOVA2
Interferon Type I

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