Phylogenetic Generalized Least Squares regression

Knowledge graph centered on Phylogenetic Generalized Least Squares regression with 34 nodes and 114 connections. Top connected: Marmota flaviventris, small mammals, Callospermophilus lateralis, Peromyscus, Marmota monax.

Method synopsis

Used phylogenetic generalized least squares (PGLS) regression and phylogenetic ANOVA to test relationships between acoustic call frequencies, body size, and habitat while controlling for evolutionary relationships among species.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — The effect of body size and habitat on the evolution of alarm vocalizations in rodents. (2016), Biological Journal of the Linnean Society. Implementations in other papers (listed below) may differ.

1. Construct phylogenetic tree
   Used a recent maximum likelihood rodent phylogeny based on 11 mitochondrial and nuclear genes (Fabre et al., 2012) to account for the shared evolutionary history between the species.
   Quantities: Phylogeny for 65 rodent species based on 11 genesDuration: Not specifiedConditions: Maximum likelihood reconstruction
   Equipment: Fabre et al. 2012 phylogeny
2. Test for phylogenetic signal
   Tested for the need to fit phylogenetic models by estimating the strength of phylogenetic signal (measured in form of Pagel's λ and Blomberg's K) in focal variables and the residuals of simple ordinary least square (OLS) regression models using a randomization test implemented in the phylotools package.
   Quantities: Pagel's lambda and Blomberg's K statisticsDuration: Not specifiedConditions: Randomization test framework
   Equipment: R phylotools package
3. Perform phylogenetic regression analyses
   Used phylogenetic generalized least square (PGLS) regression models implemented in the R package caper (Orme et al., 2013) to test for the relationship between acoustic variables and body mass. Evaluated whether an early-burst, Ornstein-Uhlenbeck or a Brownian motion (BM) model best explained the evolution of acoustic variables.

Typical Equipment

• R phylotools package
• R package caper
• R geiger package

Output Measurements

• phylogenetic signal estimates
• regression coefficients
• model fit statistics
• AIC values

Papers Using This Protocol (1)

Species Studied With This Protocol