Data from: The effects of inference method, population sampling and gene sampling on species tree inferences: an empirical study in slender salamanders (Plethodontidae: Batrachoseps)

Species tree methods are now widely used to infer the relationships among species from multi-locus datasets. Many methods have been developed, which differ in whether gene and species trees are estimated simultaneously or sequentially, and in how gene trees are used to infer the species tree. While these methods perform well on simulated data, less is known about what impacts their performance on empirical data. We used a dataset including five nuclear genes and one mitochondrial gene for 22 species of Batrachoseps to compare the effects of method of analysis, within-species sampling and gene sampling on species tree inferences. For this dataset, the choice of inference method had the largest effect on the species tree topology. Exclusion of individual loci had large effects in *BEAST and STEM, but not in MP-EST. Different loci carried the greatest leverage in these different methods, showing that the causes of their disproportionate effects differ. Even though substantial information was present in the nuclear loci, the mitochondrial gene dominated the *BEAST species tree. This leverage is inherent to the mtDNA locus and results from its high variation and lower assumed ploidy. This mtDNA leverage may be problematic when mtDNA has undergone introgression, as is likely in this dataset. By contrast, the leverage of RAG1 in STEM analyses does not reflect properties inherent to the locus, but rather results from a gene tree that is strongly discordant with all others, and is best explained by introgression between distantly related species. Within-species sampling was also important, especially in *BEAST analyses, as shown by differences in tree topology across 100 subsampled datasets. Despite the sensitivity of the species tree methods to multiple factors, five species groups, the relationships among these, and some relationships within them, are generally consistently resolved for Batrachoseps.

基于多基因座数据集推断物种间系统发育关系的物种树（species tree）构建方法，目前已得到广泛应用。现有诸多此类方法被相继开发，其差异体现在：是同时还是依次估算基因树与物种树，以及利用基因树推断物种树的具体策略。尽管这类方法在模拟数据上表现优异，但目前学界对哪些因素会影响其在实证数据中的性能仍知之甚少。本研究采用涵盖22种无肺螈属（Batrachoseps）物种的数据集开展分析，该数据集包含5个核基因与1个线粒体基因，旨在比较分析方法、种内抽样策略与基因抽样策略对物种树推断结果的影响。针对该数据集，物种树推断方法的选择对物种树拓扑结构的影响最为显著。剔除单个基因座的操作在*BEAST与STEM分析中会对结果产生显著影响，但在MP-EST分析中并无此现象。不同基因座在不同分析方法中展现出的影响力权重各不相同，这表明导致其非对称影响的根源存在差异。尽管核基因座中蕴含了大量系统发育信息，但线粒体DNA（mtDNA）主导了*BEAST方法构建的物种树。这种影响力权重是线粒体DNA基因座固有的特性，源于其较高的序列变异率与更低的假定倍性。当线粒体DNA发生遗传渐渗时，这种影响力权重可能会引发分析偏差，而该数据集的情况大概率符合这一情形。与之相反，STEM分析中重组激活基因1（RAG1）的影响力权重并非源于该基因座的固有特性，而是源于其基因树与其余所有基因树均存在强烈冲突，该现象最合理的解释是远缘物种间发生了遗传渐渗事件。种内抽样策略同样具有重要影响，尤其在*BEAST分析中，这一点可通过100个抽样子数据集得到的不同树拓扑结构得到验证。尽管物种树构建方法对多种因素存在敏感性，但针对无肺螈属（Batrachoseps）而言，其5个物种类群、类群间的亲缘关系以及部分类群内部的亲缘关系，整体上均得到了一致的解析结果。