In order to gain an understanding of the effectiveness of phylogenetic Markov
chain Monte Carlo (MCMC), it is important to understand how quickly the
empirical distribution of the MCMC converges to the posterior distribution. In
this paper we investigate this problem on phylogenetic tree topologies with a
metric that is especially well suited to the task: the subtree
prune-and-regraft (SPR) metric. This metric directly corresponds to the minimum
number of MCMC rearrangements required to move between trees in common
phylogenetic MCMC implementations. We develop a novel graph-based approach to
analyze tree posteriors and find that the SPR metric is much more informative
than simpler metrics that are unrelated to MCMC moves. In doing so we show
conclusively that topological peaks do occur in Bayesian phylogenetic
posteriors from real data sets as sampled with standard MCMC approaches,
investigate the efficiency of Metropolis-coupled MCMC (MCMCMC) in traversing
the valleys between peaks, and show that conditional clade distribution (CCD)
can have systematic problems when there are multiple peaks.

通过使用适合该任务的子树钳剪与再接（SPR）度量标准，研究了贝叶斯系统发育后分布中的拓扑峰值，并提出了一种基于图形的方法，评价了 MCMC 下的树拓扑结构，并发现在实际数据集中，存在拓扑峰值和连续多峰问题。