Phylogenetic eigenvector maps: a framework to model and predict species traits

Methods in Ecology and Evolution - Tập 4 Số 12 - Trang 1120-1131 - 2013
Guillaume Guénard1, Pierre Legendre1, Pedro R. Peres‐Neto2
1Département de sciences biologiques Universitéde Montréal CP 6128 Succ. Centre‐Ville Montréal QC H3C 3J7 Canada
2Canada Research Chair in Spatial Modeling and Biodiversity Département des sciences biologiques Universitédu Québec à Montréal CP 8888 Succ. Centre‐Ville Montréal QC H3C 3P8 Canada

Tóm tắt

Summary 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.

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Methods in Ecology and Evolution

Tập 4 Số 12

1120-1131

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