title: Testing species' deviation from allometric predictions using the phylogenetic regression.
abstract: Phylogenetic generalized least squares (PGLS) has become one of the most commonly used phylogenetic comparative methods. Despite its common use, descriptions, and applications of methods to test for species' deviations from allometric predictions using phylogenetic regression have been piecemeal. We simplify previous computational descriptions of PGLS standard errors in a manner that can be easily generalized toward more complex general linear models. We focus on the implementation of phylogenetic analysis of covariance, which provides a direct test for the equality of intercepts and slopes. Our computational descriptions allow testing whether individual species, or a group of species, deviate significantly from allometric predictions. The use of PGLS confidence and prediction intervals and phylogenetic analysis of covariance is exemplified in an analysis of brain structure volumes in primates.
authors.affiliation: Department of Anthropology, Stony Brook University, Stony Brook, New York. jeroen.smaers@stonybrook.edu.
authors.forename: J B
authors.initials: JB
authors.lastname: Smaers
authors.affiliation: Department of Anthropology, Stony Brook University, Stony Brook, New York.|Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York.
authors.forename: F J
authors.initials: FJ
authors.lastname: Rohlf
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000033:anatomy & histology*; D019143:Evolution, Molecular; D049810:Genetic Speciation; D006801:Humans; D008957:Models, Genetic; D009929:Organ Size / Q000235:genetics; D010802:Phylogeny; D011323:Primates / Q000235:genetics*
keywords: Grade shift; phylogenetic ANCOVA; phylogenetic confidence intervals; phylogenetic prediction intervals