Characterizing and comparing phylogenetic trait data from their normalized Laplacian spectrum

Abstract

The dissection of the mode and tempo of phenotypic evolution is integral to our understanding of global biodiversity. Our ability to infer patterns of phenotypes across phylogenetic clades is essential to how we infer the macroevolutionary processes governing those patterns. Many methods are already available for fitting models of phenotypic evolution to data. However, there is currently no non-parametric comprehensive framework for characterising and comparing patterns of phenotypic evolution. Here we build on a recently introduced approach for using the phylogenetic spectral density profile to compare and characterize patterns of phylogenetic diversification, in order to provide a framework for non-parametric analysis of phylogenetic trait data. We show how to construct the spectral density profile of trait data on a phylogenetic tree from the normalized graph Laplacian. We demonstrate on simulated data the utility of the spectral density profile to successfully cluster phylogenetic trait data into meaningful groups and to characterise the phenotypic patterning within those groups. We furthermore demonstrate how the spectral density profile is a powerful tool for visualising phenotypic space across traits and for assessing whether distinct trait evolution models are distinguishable on a given empirical phylogeny. We illustrate the approach in two empirical datasets: a comprehensive dataset of traits involved in song, plumage and resource-use in tanagers, and a high-dimensional dataset of endocranial landmarks in New World monkeys. Considering the proliferation of morphometric and molecular data collected across the tree of life, we expect this approach will benefit big data analyses requiring a comprehensive and intuitive framework.Phylogenetic trait data are essential to understanding the evolution of biodiversity. 1 They have been used to identify adaptive radiations (Harmon et al. 2010), infer stabilizing 2 selection (Hansen 1997; Butler and King 2004), measure the phenotypic effects of species 3 interactions (Drury et al. 2018) and environmental fluctuations (Clavel and Morlon 2017), 4 and generally to estimate the role of the phylogeny in how traits evolve over time 5 ( Felsenstein, 1973). They are critical to connecting microevolutionary processes of natural 6 selection to macroevolutionary patterns of phenotypic evolution (Hansen and Martins 7 1996). 8A wide range of approaches, reflecting the general interest of trait evolution among 9 evolutionary biologists, have been developed to infer the mode and tempo of phenotypic 10 evolution across clades. These include summary statistics that test for the degree of 11 phylogenetic signal in trait data, such as Blomberg's K (Blomberg et al. 2003), and 12 maximum likelihood-based techniques that fit models to phylogenetic trait data and 13 estimate the rate at which traits evolve (see Pennell and Harmon (2013); Manceau et al. 14 (2016); Lewitus (2018) for a review of currently available models). These models rely on 15 the a priori formulation of a phenotypic mo...

Understanding the effect of competition during evolutionary radiations: an integrated model of phenotypic and species diversification

“…It would be interesting to test if novel statistics that capture finer scale variation in a trait’s distribution than Blomberg’s K (e.g. Lewitus et al in press) are also able to distinguish these two scenarios, as well as to check if phylogenetic signal remains a feature of adaptive radiations in multidimensional trait space (Harvey & Rambaut , Doebeli & Ispolatov ). Likewise, it would be useful to assess the robustness of the support for Drury et al .’s MC models (2016) with a variety of competition‐driven radiation scenarios, as it could be argued that our generating model resembles the MC inference tool.…”

PhyGraFT: a network-based method for phylogenetic trait analysis

“…In this research, we developed a novel network-thinking approach PhyGraFT to identify evolutionary signals and associations of traits based on the graph signaling process framework. Several graph Laplacian-based methods have been proposed for evolutionary studies (21)(22)(23)(24), and this research is a further development of these approaches by introducing the GFT framework into phylogenetic analysis. We applied PhyGraFT for influenza type A virus datasets and virome gene-sharing dataset and demonstrated that we could locate various evolutionary structures and these associated traits from similarity networks.…”

“…For example, the eigenvalues of the graph Laplacian derived from the phylogenetic distance matrices are used for classifying phylogenetic trees (21, 22). This approach has also been extended to analyze phylogenetic trait evolution by constructing a graph Laplacian, considering both evolutionary and trait similarity (23). Moreover, a novel phylogenetic tree recon-struction method based on the graph Laplacian of sequence similarity was proposed (24).…”