1. It has become common in evolutionary biology to characterize phenotypes multivariately. However, visualizing macroevolutionary trends in multivariate datasets requires appropriate ordination methods.
2. 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.
3. 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.
4. 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.
5. 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.

中文翻译：

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系统发育比对的成分分析

1. 多态性表征表型在进化生物学中已经很普遍。但是，可视化多元数据集中的宏观进化趋势需要适当的排序方法。
2. 在本文中，我们描述了系统发育比对成分分析（PACA）：一种将表型数据与系统发生信号比对的新排序方法。与系统发育主成分分析（Phy-PCA）可以找到与系统发生信号无关的主要特征向量的对齐方式不同，PACA可以最大化描述系统发生信号方向的变化，同时保留数据空间中观测值之间的欧几里得距离。
3. 我们通过模拟和经验示例证明，使用PACA，可以可视化多元数据空间中系统发生信号的趋势，而与数据中的其他信号无关。与Phy-PCA结合使用，可以将系统发生信号和数据趋势可视化，而与系统发生信号无关。
4. 系统发育对齐的成分分析可以区分仅在所有数据维度中一部分集中的弱系统发育信号和强信号。我们提供了强调差异的经验示例。结果，在专注于系统发生信号的研究中使用PACA应该能够更精确地描述系统发生信号。
5. 总体而言，无论数据中的其他信号如何，PACA都会返回一个在前几个组件中显示出最多系统发育信号的投影。通过比较Phy-PCA和PACA结果，可以收集系统发育和其他（生态）信号在数据中的相对重要性。