On the selection of phylogenetic eigenvectors for ecological analyses

By J. Diniz-Filho, Luis Mauricio Bini, Thiago Fernando Rangel, I. Morales‐Castilla, M. Á. Olalla‐Tárraga, M. Rodríguez, B. Hawkins. 2012

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Among the statistical methods available to control for phylogenetic autocorrelation in ecological data, those based on eigenfunction analysis of the phylogenetic distance matrix among the species are becoming increasingly important tools. Here, we evaluate a range of criteria to select eigenvectors extracted from a phylogenetic distance matrix (using phylogenetic eigenvector regression, PVR) that can be used to measure the level of phylogenetic signal in ecological data and to study correlated evolution. We used a principal coordinate analysis to represent the phylogenetic relationships among 209 species of Carnivora by a series of eigenvectors, which were then used to model log-transformed body size. We fi rst conducted a series of PVRs in which we increased the number of eigenvectors from 1 to 70, following the sequence of their associated eigenvalues. Second, we also investigated three non-sequential approaches based on the selection of 1) eigenvectors signifi cantly correlated with body size, 2) eigenvectors selected by a standard stepwise algorithm, and 3) the combination of eigenvectors that minimizes the residual phylogenetic autocorrelation. We mapped the mean specifi c component of body size to evaluate how these selection criteria aff ect the interpretation of non-phylogenetic signal in Bergmann ’ s rule. For comparison, the same patterns were analyzed using autoregressive model (ARM) and phylogenetic generalized leastsquares (PGLS). Despite the robustness of PVR to the specifi c approaches used to select eigenvectors, using a relatively small number of eigenvectors may be insuffi cient to control phylogenetic autocorrelation, leading to fl awed conclusions about patterns and processes. Th e method that minimizes residual autocorrelation seems to be the best choice according to diff erent criteria. Th us, our analyses show that, when the best criterion is used to control phylogenetic structure, PVR can be a valuable tool for testing hypotheses related to heritability at the species level, phylogenetic niche conservatism and correlated evolution between ecological traits.

Subfields: Ecology, Geometry and Topology, Paleontology

View Paper: https://www.semanticscholar.org/paper/bfcb0da0621778c986a802ed960a534ec026625d

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Citations in the corpus

Decoupling phylogenetic and functional diversity to reveal hidden signals in community assembly (2017, methodology): “Accordingly, a combination of these two aspects of species differentiation has been encouraged (Kraft et al. 2007; Prinzing et al. 2008; Pillar & Duarte 2010; Pavoine et al. 2011; Diniz-Filho et al. 2012; Cadotte, Albert & Walker 2013).”
Moving beyond the guild concept: developing a practical functional trait framework for terrestrial beetles (2015, background): “Phylogenetic eigenvector regression (PVR) is a promising way to control for phylogenetic autocorrelation in quantitative (and qualitative) trait data whilst maintaining species identity [see Desdevises et al. (2003); Diniz-Filho et al. (2012) for method details].”
Disentangling good from bad practices in the selection of spatial or phylogenetic eigenvectors (2018, methodology): “The AIC approach was originally developed by Dray et al. (2006) to select a spatial weighting matrix W among a range of candidates.”