Abstract
Confirmatory path analysis allows researchers to evaluate and compare causal models using observational data. This tool has great value for comparative biologists since they are often unable to gather experimental data on macro-evolutionary hypotheses, but is cumbersome and error-prone to perform. I introduce phylopath, an R package that implements phylogenetic path analysis (PPA) as described by von Hardenberg & Gonzalez-Voyer (2013). In addition to the published method, I provide support for the inclusion of binary variables. I illustrate PPA and phylopath by recreating part of a study on the relationship between brain size and vulnerability to extinction. The package aims to make the analysis straight-forward, providing convenience functions, and several plotting methods, which I hope will encourage the spread of the method.
Highlights
The comparative method is a critical tool to answer macro-evolutionary questions and has been since the start of evolutionary biology itself (Darwin, 1839)
This study focused on the possible influence of brain size on the vulnerability to extinction in 474 mammalian species
This effect is mediated through life history, where the weaning and gestation periods are more important than litter size
Summary
The comparative method is a critical tool to answer macro-evolutionary questions and has been since the start of evolutionary biology itself (Darwin, 1839). In comparative biology normal regression models cannot be used for path analysis since the assumption of independence of observations is violated, as closely related species are expected to be more similar (Felsenstein, 1985; Pagel & Harvey, 1991). For the worked exercise in the book chapter outlining the method (Gonzalez-Voyer & von Hardenberg, 2014), the reader needs to define a list of 46 total d-separation statements and fit 21 PGLS models, and compile the results afterwards This takes a lot of time, requires a lot of code and the number of steps required increases the chance for errors. I hope that a specialized software implementation will greatly increase the reproducibility of the method, decrease research effort to perform the analysis and encourage the spread of the method by decreasing entry barriers
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