Phylogenetic Comparative Methods and the Evolution of Multivariate Phenotypes

Abstract

Evolutionary biology is multivariate, and advances in phylogenetic comparative methods for multivariate phenotypes have surged to accommodate this fact. Evolutionary trends in multivariate phenotypes are derived from distances and directions between species in a multivariate phenotype space. For these patterns to be interpretable, phenotypes should be characterized by traits in commensurate units and scale. Visualizing such trends, as is achieved with phylomorphospaces, should continue to play a prominent role in macroevolutionary analyses. Evaluating phylogenetic generalized least squares (PGLS) models (e.g., phylogenetic analysis of variance and regression) is valuable, but using parametric procedures is limited to only a few phenotypic variables. In contrast, nonparametric, permutation-based PGLS methods provide a flexible alternative and are thus preferred for high-dimensional multivariate phenotypes. Permutation-based methods for evaluating covariation within multivariate phenotypes are also well established and can test evolutionary trends in phenotypic integration. However, comparing evolutionary rates and modes in multivariate phenotypes remains an important area of future development.

The evolution of red blood cell shape in a continental radiation of fishes

“…Astyanax also occurs in habitats with hypoxia (Froese and Pauly, 2019). The pattern we found points to clade disparity differences (Adams & Collyer 2019), with Acanthopterygii having higher dispersion in the morphospace than Ostariophysi (Fig. S6).…”

“…The low effect size suggest phylogenetic signal is not concentrated in a few dimensions. To test if only a subset of dimensions displays phylogenetic signal (see Adams & Collyer 2019), we re-run the analysis with only the first two pPCs. Results did not change (see Martins et al 2020).…”

“…Most species occupied the center of the morphospace, independent of the phylogenetic relationship. This clustering of distantly related species suggests that convergence, apparently not caused by water column position, could be a mechanism influencing RBS shape evolution (Adams and Collyer 2019). Clustering in morphospace can also suggest a developmental constraint (Jablonski 2020) that impedes species to change their traits after speciation (Pie and Weitz 2005).…”

“…The pPCA was performed using the correlation matrix of variables and assuming that traits have evolved according to a Brownian Motion (BM) model of evolution. The pPCA uses a C matrix that describes the variance-covariance structure between species given by the phylogeny and the evolutionary model (BM in this case) to obtain the Principal Components and species scores (see also Adams & Collyer 2019). Analysis was performed using the function of the phytools package (Revell, 2012).…”

“…The analysis estimates the node values using a Maximum Likelihood method. Finally, we built the phylomorphospace by projecting the phylogeny onto the morphospace built with the first two Principal Components, which allows us to explore how lineages occupied the morphospace as their traits evolved (Adams and Coller 2018, 2019). Analysis was performed using the function of the phytools package.…”