Data from: Phylogenetic ANOVA: group-clade aggregation, biological challenges, and a refined permutation procedure

Phylogenetic regression is frequently utilized in macroevolutionary studies, and its statistical properties have been thoroughly investigated. By contrast, phylogenetic ANOVA has received relatively less attention, and the conditions leading to incorrect statistical and biological inferences when comparing multivariate phenotypes among groups remains under-explored. Here we propose a refined method of randomizing residuals in a permutation procedure (RRPP) for evaluating phenotypic differences among groups while conditioning the data on the phylogeny. We show that RRPP displays appropriate statistical properties for both phylogenetic ANOVA and regression models, and for univariate and multivariate datasets. For ANOVA, we find that RRPP exhibits higher statistical power than methods utilizing phylogenetic simulation. Additionally, we investigate how group dispersion across the phylogeny affects inferences, and reveal that highly aggregated groups generate strong and significant correlations with the phylogeny, which reduce statistical power and subsequently affect biological interpretations. We discuss the broader implications of this phylogenetic group aggregation, and its relation to challenges encountered with other comparative methods where one or a few transitions in discrete traits are observed on the phylogeny. Finally, we recommend that phylogenetic comparative studies of continuous trait data utilize RRPP for assessing the significance of indicator variables as sources of trait variation.

系统发育回归（phylogenetic regression）在宏观演化研究中应用极为广泛，其统计性质已得到全面细致的探究。相较而言，系统发育方差分析（phylogenetic ANOVA）受到的关注相对不足，而在组间多变量表型比较过程中，导致统计与生物学推断出现错误的条件仍未得到充分探索。本研究提出一种改良的置换残差随机化方法（randomizing residuals in a permutation procedure, RRPP），用于在基于系统发育对数据进行条件化处理的前提下，评估组间表型差异。研究表明，RRPP在系统发育方差分析、回归模型以及单变量、多变量数据集场景下均展现出合格的统计性质。针对方差分析场景，本研究发现RRPP的统计功效高于基于系统发育模拟的方法。此外，本研究探究了组在系统发育树上的分散程度对推断结果的影响，并揭示出高度聚集的组会与系统发育产生强且显著的相关性，这会降低统计功效，进而影响生物学解释的合理性。本研究还探讨了这种系统发育组聚集现象的更广泛意义，以及其与其他比较方法所面临挑战的关联：这类挑战通常体现为在系统发育树上观测到离散性状的一次或少数几次演化转变。最后，本研究建议，针对连续性状数据的系统发育比较研究，应采用RRPP来评估指示变量作为性状变异来源的显著性。