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Characterizing and Comparing Phylogenetic Trait Data from Their Normalized Laplacian Spectrum
Systematic Biology ( IF 6.5 ) Pub Date : 2019-09-16 , DOI: 10.1093/sysbio/syz061
Eric Lewitus 1, 2 , Leandro Aristide 1 , Hélène Morlon 1
Affiliation  

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 comprehensive non-parametric 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.

中文翻译:

从标准化拉普拉斯谱表征和比较系统发育特征数据

表型进化模式和节奏的剖析是我们理解全球生物多样性不可或缺的一部分。我们推断系统发育进化枝表型模式的能力对于我们如何推断控制这些模式的宏观进化过程至关重要。许多方法已经可用于将表型进化模型拟合到数据。然而,目前没有用于表征和比较表型进化模式的综合非参数框架。在这里,我们建立在最近引入的使用系统发育谱密度剖面来比较和表征系统发育多样化模式的方法的基础上,以便为系统发育性状数据的非参数分析提供框架。我们展示了如何从归一化的拉普拉斯图构建系统发育树上特征数据的谱密度分布。我们在模拟数据上证明了光谱密度剖面的效用,可以成功地将系统发育特征数据聚类到有意义的组中,并表征这些组内的表型模式。我们进一步展示了光谱密度剖面如何成为可视化跨性状的表型空间以及评估不同的性状进化模型在给定的经验系统发育上是否可区分的强大工具。我们在两个经验数据集中说明了该方法:一个综合数据集涉及唐雀的歌曲、羽毛和资源使用的特征,以及一个新世界猴子的颅内标志的高维数据集。
更新日期:2019-09-16
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