Article

Comparative methods as a statistical fix: the dangers of ignoring an evolutionary model.

Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, United Kingdom.
The American Naturalist (Impact Factor: 4.45). 07/2011; 178(1):E10-7. DOI: 10.1086/660272
Source: PubMed

ABSTRACT Abstract Comparative methods are widely used in ecology and evolution. The most frequently used comparative methods are based on an explicit evolutionary model. However, recent approaches have been popularized that are without an evolutionary basis or an underlying null model. Here we highlight the limitations of such techniques in comparative analyses by using simulations to compare two commonly used comparative methods with and without evolutionary basis, respectively: generalized least squares (GLS) and phylogenetic eigenvector regression (PVR). We find that GLS methods are more efficient at estimating model parameters and produce lower variance in parameter estimates, lower phylogenetic signal in residuals, and lower Type I error rates than PVR methods. These results can very likely be generalized to eigenvector methods that control for space and both space and phylogeny. We highlight that GLS methods can be adapted in numerous ways and that the variance structure used in these models can be flexibly optimized to each data set.

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    • "A method for incorporating phylogeny into assemblage-level grid-cell analyses, phylogenetic eigenvector regression, exists (Diniz-Filho et al., 1998). However , this method has severe statistical limitations and probably does not adequately account for the effects of phylogeny (Adams & Church, 2011; Freckleton et al., 2011). "
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    • "We chose four different measures commonly used to estimate phylogenetic signal in evolutionary biology and ecology – see Münkemüller et al. (2012) and references therein for a comprehensive review: Pagel's λ (Pagel, 1999), Abouheif's C mean (Abouheif, 1999), Blomberg's K (Blomberg, Garland & Ives, 2003), and Diniz-Filho's phylogenetic eigenvector regressions (PVR; Diniz-Filho, de Sant'Ana & Bini, 1998). The efficiency of PVR has been criticised in recent literature (Adams & Church, 2011; Freckleton, Cooper & Jetz, 2011). However, Diniz-Filho et al. (2012) found PVR to perform well with an appropriate phylogenetic eigenvector selection procedure, and suggested directly minimizing residual Moran's I as a powerful iterative approach; for this reason, we used this method for eigenvector selection. "
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