Data from: Implementing and testing Bayesian and Maximum likelihood supertree methods in phylogenetics

Since their advent, supertrees have been increasingly used in large-scale evolutionary studies requiring a phylogenetic framework and substantial efforts have been devoted to developing a wide variety of supertree methods (SM). Recent advances in supertree theory have allowed the implementation of Maximum Likelihood (ML) and Bayesian SMs, based on using an exponential distribution to model incongruence between input trees and the supertree. Such approaches are expected to have advantages over commonly used non-parametric SMs e.g. Matrix Representation with Parsimony (MRP). We investigated new implementations of ML and Bayesian SMs and compared these with some currently available alternative approaches. Comparisons include hypothetical examples previously used to investigate biases of SMs with respect to input tree shape and size, and empirical studies based either on trees harvested from the literature or on trees inferred from phylogenomic scale data. Our results provide no evidence of size or shape biases and demonstrate that the Bayesian method is a viable alternative to MRP and other non-parametric methods. Computation of input tree likelihoods allows the adoption of standard tests of tree topologies (e.g. the approximately unbiased test). The Bayesian approach is particularly useful in providing support values for supertree clades in the form of posterior probabilities.自超树（supertree）问世以来，其在需要系统发育框架的大规模进化研究中的应用日益广泛，学界亦投入大量精力开发了种类繁多的超树方法（supertree methods, SM）。近年来超树理论的进展推动了最大似然（Maximum Likelihood, ML）与贝叶斯超树方法的实现，这类方法基于指数分布对输入树与超树间的不一致性进行建模。相较于简约矩阵表示法（Matrix Representation with Parsimony, MRP）等常用的非参数超树方法，此类方法被认为具备显著优势。本研究针对最大似然与贝叶斯超树方法的新实现方案展开探究，并将其与当前主流的替代方法进行对比。对比对象既包括此前用于探究超树方法针对输入树形态与规模偏差的假想示例，也涵盖基于文献收录树或系统基因组规模数据推导所得树结构的实证研究。研究结果未显示超树方法存在规模或形态偏差，同时证明贝叶斯方法可作为简约矩阵表示法及其他非参数方法的可靠替代方案。通过计算输入树的似然值，可采用标准的树拓扑结构检验方法（如近似无偏检验（approximately unbiased test））。贝叶斯方法的另一突出优势在于，能够以后验概率的形式为超树支系提供支持值。