Phylogenetic eigenvector maps: a framework to model and predict species traits
Article first published online: 23 OCT 2013
© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society
Methods in Ecology and Evolution
Volume 4, Issue 12, pages 1120–1131, December 2013
How to Cite
Guénard, G., Legendre, P., Peres-Neto, P. (2013), Phylogenetic eigenvector maps: a framework to model and predict species traits. Methods in Ecology and Evolution, 4: 1120–1131. doi: 10.1111/2041-210X.12111
- Issue published online: 9 DEC 2013
- Article first published online: 23 OCT 2013
- Accepted manuscript online: 10 SEP 2013 03:36AM EST
- Manuscript Accepted: 21 AUG 2013
- Manuscript Received: 11 JAN 2013
- NSERC. Grant Number: 7738
- comparative method;
- evolutionary models;
- graph theory;
- Ornstein–Uhlenbeck process;
- phylogenetic eigenvectors;
- phylogenetic modelling;
- phylogenetic signal;
- statistical modelling;
- trait values
- Phylogenetic signals are the legacy related to evolutionary processes shaping trait variation among species. Biologists can use these signals to tackle questions related to the evolutionary processes underlying trait evolution, estimate the ancestral state of a trait and predict unknown trait values from those of related species (i.e. ‘phylogenetic modelling’). Approaches to model phylogenetic signals rely on quantitative descriptors of the structures representing the consequences of evolution on trait differences among species.
- Here, we propose a novel framework to model phylogenetic signals: Phylogenetic Eigenvectors Maps (PEM). PEM are a set of eigenfunctions obtained from the structure of a phylogenetic graph, which can be a standard phylogenetic tree or a phylogenetic tree with added reticulations. These eigenfunctions depict a set of potential patterns of phenotype variation among species from the structure of the phylogenetic graph. A subset of eigenfunctions from a PEM is selected for the purpose of predicting the phenotypic values of traits for species that are represented in a tree, but for which trait data are otherwise lacking. This paper introduces a comprehensive view and the computational details of the PEM framework (with calculation examples), a simulation study to demonstrate the ability of PEM to predict trait values and four real data examples of the use of the framework.
- Simulation results show that PEM are robust in representing phylogenetic signal and in estimating trait values.
- The method also performed well when applied to the real-world data: prediction coefficients were high (0·76–0·88), and no notable model biases were found.
- Phylogenetic modelling using PEM is shown to be a useful methodological asset to disciplines such as ecology, ecophysiology, ecotoxicology, pharmaceutical botany, among others, which can benefit from estimating trait values that are laborious and often expensive to obtain.