On the analysis of phylogenetically paired designs. Issue 4 (30th January 2015)

• Record Type: Journal Article
• Authors: Funk, Jennifer L.; Rakovski, Cyril S.; Macpherson, J. Michael
• Is Part Of: Ecology and evolution. Volume 5:Issue 4(2015:Apr.)
• Page Start: 940
• Page End: 947
• Publication Date: 2015-01-30
• Subjects: Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.1406
• Languages: English
• ISSNs: 2045-7758

Abstract

<abstract abstract-type="main" id="ece31406-abs-0001"> <title>Abstract</title> <sec id="ece31406-sec-0001" sec-type="section"> <p>As phylogenetically controlled experimental designs become increasingly common in ecology, the need arises for a standardized statistical treatment of these datasets. Phylogenetically paired designs circumvent the need for resolved phylogenies and have been used to compare species groups, particularly in the areas of invasion biology and adaptation. Despite the widespread use of this approach, the statistical analysis of paired designs has not been critically evaluated. We propose a mixed model approach that includes random effects for pair and species. These random effects introduce a "two‐layer" compound symmetry variance structure that captures both the correlations between observations on related species within a pair as well as the correlations between the repeated measurements within species. We conducted a simulation study to assess the effect of model misspecification on Type I and II error rates. We also provide an illustrative example with data containing taxonomically similar species and several outcome variables of interest. We found that a mixed model with species and pair as random effects performed better in these phylogenetically explicit simulations than two commonly used reference models (no or single random effect) by optimizing Type I error rates and power. The proposed mixed model produces acceptable Type I and II error rates despite the absence of a phylogenetic tree. This design can be generalized to a variety of datasets to analyze repeated measurements in clusters of related subjects/species.</p> </sec> </abstract>