Data from: Probabilistic distances between trees

Most existing measures of distance between phylogenetic trees are based on the geometry or topology of the trees. Instead, we consider distance measures which are based on the underlying probability distributions on genetic sequence data induced by trees. Monte Carlo schemes are necessary to calculate these distances approximately, and we describe efficient sampling procedures. Key features of the distances are the ability to include substitution model parameters and to handle trees with different taxon sets in a principled way. We demonstrate some of the properties of these new distance measures and compare them to existing distances, in particular by applying multidimensional scaling to data sets previously reported as containing phylogenetic islands.

当前主流的系统发育树（phylogenetic tree）间距离测度多基于树的几何结构或拓扑结构。与之不同，本文所探讨的距离测度以树所诱导的遗传序列数据的底层概率分布为基础。若要近似计算此类距离，需借助蒙特卡洛（Monte Carlo）方法，本文将阐述其中的高效采样流程。该类距离测度的核心特性在于，可纳入置换模型参数，且能以严谨规范的方式处理分类群集合存在差异的系统发育树。本文将展示这类新型距离测度的部分属性，并与现有距离测度进行对比，尤其通过将多维标度（multidimensional scaling）应用于此前被报道存在系统发育岛（phylogenetic island）的数据集来开展对比分析。