Data from: Experimental design in phylogenetics: testing predictions from expected information

Taxon and character sampling is central to phylogenetic experimental design yet we lack general rules. Goldman introduced a method to construct efficient sampling designs in phylogenetics, based on the calculation of expected Fisher information given a probabilistic model of sequence evolution. The considerable potential of this approach remains largely unexplored. In an earlier study, we applied Goldman’s method to a problem in the phylogenetics of caecilian amphibians and made an a priori evaluation and testable predictions of which taxon additions would increase information about a particular weakly supported branch of the caecilian phylogeny by the greatest amount. Using mitogenomic and rag1 sequences (some newly determined for this study) from additional caecilian species we studied how information (both expected and observed) and bootstrap support varies as each new taxon is individually added, providing the first empirical test of specific predictions made using Goldman’s method for phylogenetic experimental design. Our results empirically validate the top three (more intuitive) taxon addition predictions made in our previous study, but only information results validate unambiguously the fourth (less intuitive) prediction. This highlights a complex relationship between information and support, reflecting that each measures different things: information is related to the ability to estimate branch length accurately, and support to the ability to estimate the tree topology accurately. Thus, an increase in information may be correlated with but does not necessitate an increase in support Our results also provide the first empirical validation of the widely held intuition that additional taxa that join the tree proximal to poorly supported internal branches are more informative and enhance support more than additional taxa that join the tree more distally. Our work supports the view that adding more data for a single (well chosen) taxon may increase phylogenetic resolution and support in weakly supported parts of the tree without adding more characters/genes while illustrating that less well chosen taxon additions can have the opposite effect. Altogether our results corroborate that, although still underexplored, Goldman’s method offers a powerful tool for experimental design in molecular phylogenetic studies. However, there are still several drawbacks to overcome, and further assessment of the method is needed in order to make it better understood, more accessible, and able to assess additions of multiple taxa.类群与特征采样是系统发育学（phylogenetics）实验设计的核心环节，但当前仍缺乏普适性的设计准则。高盛（Goldman）基于序列演化概率模型（probabilistic model of sequence evolution）下的期望费舍尔信息（expected Fisher information）计算，提出了一套系统发育学高效采样设计方法，然而该方法的巨大潜力迄今仍未得到充分发掘。 在既往一项研究中，我们将高盛的方法应用于蚓螈类两栖动物（caecilian amphibians）的系统发育学问题，先验性地评估了哪些类群的新增能最大程度提升针对蚓螈系统发育中某一支支持度较弱分支的信息，并生成了可验证的预测结果。本研究针对额外的蚓螈类物种，使用线粒体基因组序列（mitogenomic）与重组激活基因1（rag1）序列（其中部分序列为本研究新测定），探究了逐个新增类群时，信息（包括期望信息与实测信息）与自展支持率（bootstrap support）的变化情况，首次对基于高盛方法提出的系统发育实验设计具体预测开展了实证检验。 研究结果从实证层面验证了我们前期研究中提出的前三项（更符合直觉的）类群新增预测，但仅信息维度的结果明确验证了第四项（较不符合直觉的）预测。这凸显了信息与支持度之间的复杂关联，二者的衡量维度存在差异：信息与分支长度的准确估算能力相关，而支持度则与系统发育拓扑结构（tree topology）的准确估算能力相关。因此，信息的提升可能与支持度的提升呈正相关，但并非必然导致支持度提升。 本研究结果同时首次实证验证了学界广泛认同的直觉：相较于在系统发育树中距离支持度较弱的内部节点更远的位置新增类群，在该类节点附近新增类群能带来更多的信息增益，并能更显著地提升支持度。 我们的研究支持这一观点：针对单个（筛选得当的）类群补充测序数据，无需增加更多特征/基因，即可提升系统发育树中支持度较弱区域的分辨率与支持度；同时也表明，筛选不当的类群新增反而可能产生相反效果。 综上，尽管仍未得到充分探索，但高盛的方法为分子系统发育学研究的实验设计提供了强有力的工具。然而该方法仍存在若干亟待解决的缺陷，未来需开展进一步评估，以提升其可理解性、易用性，并使其能够实现多类群新增的评估工作。