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.

物种树推断方法目前已被广泛应用于基于多位点数据集的物种间演化关系解析。目前已开发出多种物种树推断方法，其差异主要体现在两个维度：一是基因树与物种树的估计方式（同时估计或依次估计），二是利用基因树推导物种树的具体策略。尽管此类方法在模拟数据集上表现良好，但学界对哪些因素会影响其在实际观测数据中的性能仍缺乏足够认知。本研究选取包含22种棒螈属（Batrachoseps）物种的5个核基因与1个线粒体基因数据集，旨在对比分析方法选择、种内采样策略与基因采样策略对物种树推断结果的影响。针对该数据集，推断方法的选择对物种树拓扑结构的影响最为显著。剔除单个基因位点会对*BEAST与STEM的分析结果产生显著影响，但对MP-EST分析则无此效应。不同基因位点在这三类方法中发挥的影响力各不相同，表明这类不成比例影响的成因存在差异。尽管核基因位点已包含丰富的演化信息，但线粒体基因在*BEAST构建的物种树中占据主导地位。这种主导影响力是线粒体DNA（mtDNA）位点固有的特性，源于其较高的序列变异率与较低的假定倍性。若线粒体DNA发生渐渗（introgression）——该数据集极有可能存在此类事件——则这种主导影响力可能会引发分析偏差。与之形成对比的是，STEM分析中RAG1基因所发挥的影响力并非源于该位点本身的固有特性，而是源于其基因树与其他所有基因树存在强烈冲突，这一现象最合理的解释是远缘物种间发生了渐渗。种内采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个次采样数据集间物种树拓扑结构的显著差异得到验证。尽管物种树推断方法对多种因素存在敏感性，但棒螈属（Batrachoseps）的5个物种群、群间演化关系以及部分群内演化关系，整体上均得到了一致的推断结果。