A primer on phylogenetic generalised least squares

Symonds, Matthew R. E. and Blomberg, Simon P. (2014). A primer on phylogenetic generalised least squares. In Laszlo Zsolt Garamszegi (Ed.), Modern phylogenetic comparative methods and their application in evolutionary biology: concepts and practice (pp. 105-130) Berlin Heidelberg, Germany: Springer. doi:10.1007/978-3-662-43550-2_5

Author Symonds, Matthew R. E.
Blomberg, Simon P.
Title of chapter A primer on phylogenetic generalised least squares
Title of book Modern phylogenetic comparative methods and their application in evolutionary biology: concepts and practice
Place of Publication Berlin Heidelberg, Germany
Publisher Springer
Publication Year 2014
Sub-type Research book chapter (original research)
DOI 10.1007/978-3-662-43550-2_5
Open Access Status
Year available 2014
ISBN 9783662435496
Editor Laszlo Zsolt Garamszegi
Chapter number 5
Start page 105
End page 130
Total pages 26
Total chapters 22
Collection year 2015
Language eng
Formatted Abstract/Summary
Phylogenetic generalised least squares (PGLS) is one of the most commonly employed phylogenetic comparative methods. The technique, a modification of generalised least squares, uses knowledge of phylogenetic relationships to produce an estimate of expected covariance in cross-species data. Closely related species are assumed to have more similar traits because of their shared ancestry and hence produce more similar residuals from the least squares regression line. By taking into account the expected covariance structure of these residuals, modified slope and intercept estimates are generated that can account for interspecific autocorrelation due to phylogeny. Here, we provide a basic conceptual background to PGLS, for those unfamiliar with the approach. We describe the requirements for a PGLS analysis and highlight the packages that can be used to implement the method. We show how phylogeny is used to calculate the expected covariance structure in the data and how this is applied to the generalised least squares regression equation. We demonstrate how PGLS can incorporate information about phylogenetic signal, the extent to which closely related species truly are similar, and how it controls for this signal appropriately, thereby negating concerns about unnecessarily ‘correcting’ for phylogeny. In addition to discussing the appropriate way to present the results of PGLS analyses, we highlight some common misconceptions about the approach and commonly encountered problems with the method. These include misunderstandings about what phylogenetic signal refers to in the context of PGLS (residuals errors, not the traits themselves), and issues associated with unknown or uncertain phylogeny.
Q-Index Code BX
Q-Index Status Confirmed Code
Institutional Status UQ

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Created: Mon, 09 Mar 2015, 11:35:46 EST by Simon Blomberg on behalf of School of Biological Sciences