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Abstract Evolutionary biologists characterize macroevolutionary trends of phenotypic change across the tree of life using phylogenetic comparative methods. However, within‐species variation can complicate such investigations. For this reason, procedures for incorporating nonstructured (random) intraspecific variation have been developed.Likewise, evolutionary biologists seek to understand microevolutionary patterns of phenotypic variation within species, such as sex‐specific differences or allometric trends. Additionally, there is a desire to compare such within‐species patterns across taxa, but current analytical approaches cannot be used to interrogate within‐species patterns while simultaneously accounting for phylogenetic non‐independence. Consequently, deciphering how intraspecific trends evolve remains a challenge.Here we introduce an extended phylogenetic generalized least squares (E‐PGLS) procedure which facilitates comparisons of within‐species patterns across species while simultaneously accounting for phylogenetic non‐independence.Our method uses an expanded phylogenetic covariance matrix, a hierarchical linear model, and permutation methods to obtain empirical sampling distributions and effect sizes for model effects that can evaluate differences in intraspecific trends across species for both univariate and multivariate data, while conditioning them on the phylogeny.The method has appropriate statistical properties for both balanced and imbalanced data. Additionally, the procedure obtains evolutionary covariance estimates that reflect those from existing approaches for nonstructured intraspecific variation. Importantly, E‐PGLS can detect differences in structured (i.e. microevolutionary) intraspecific patterns across species when such trends are present. Thus, E‐PGLS extends the reach of phylogenetic comparative methods into the intraspecific comparative realm, by providing the ability to compare within‐species trends across species while simultaneously accounting for shared evolutionary history.
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Phylogenetically aligned component analysis
Abstract It has become common in evolutionary biology to characterize phenotypes multivariately. However, visualizing macroevolutionary trends in multivariate datasets requires appropriate ordination methods.In this paper we describe phylogenetically aligned component analysis (PACA): a new ordination approach that aligns phenotypic data with phylogenetic signal. Unlike phylogenetic principal component analysis (Phy‐PCA), which finds an alignment of a principal eigenvector that is independent of phylogenetic signal, PACA maximizes variation in directions that describe phylogenetic signal, while simultaneously preserving the Euclidean distances among observations in the data space.We demonstrate with simulated and empirical examples that with PACA, it is possible to visualize the trend in phylogenetic signal in multivariate data spaces, irrespective of other signals in the data. In conjunction with Phy‐PCA, one can visualize both phylogenetic signal and trends in data independent of phylogenetic signal.Phylogenetically aligned component analysis can distinguish between weak phylogenetic signals and strong signals concentrated in only a portion of all data dimensions. We provide empirical examples that emphasize the difference. Use of PACA in studies focused on phylogenetic signal should enable much more precise description of the phylogenetic signal, as a result.Overall, PACA will return a projection that shows the most phylogenetic signal in the first few components, irrespective of other signals in the data. By comparing Phy‐PCA and PACA results, one may glean the relative importance of phylogenetic and other (ecological) signals in the data.
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- PAR ID:
- 10453530
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 12
- Issue:
- 2
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 359-372
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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