Figure_S5

Clustering on principal components and spectral density profiles from the nMGL. (a) Hierarchical clustering on spectral density profiles and (b) k-medoids clustering on principal components are computed as in Figure 3, except based on the nMGL. In (A), hierarchical clustering on the spectral density profile identified six clusters of trees (bootstrap probability > 0.95), each corresponding to a distinct underlying diversification model, whose property in terms of speciation-extinction rate variation is summarized in the left column. In (b), only four significant clusters were identified (P < 0.05).

基于nMGL的主成分（principal components）与谱密度分布（spectral density profiles）聚类分析。(a) 基于谱密度分布的层次聚类（hierarchical clustering）与(b) 基于主成分的k中心点聚类（k-medoids clustering）的计算流程与图3一致，仅以nMGL作为分析数据源。在(A)中，基于谱密度分布的层次聚类共识别出6组系统发育树（自助法概率（bootstrap probability）> 0.95），每组对应一种独特的潜在物种分化模型，其物种形成-灭绝速率（speciation-extinction rate）的变异特征已在左侧列中汇总。在(b)中，仅识别出4个具有统计学显著性的聚类（P < 0.05）。