Selecting and averaging relaxed clock models in Bayesian tip dating of Mesozoic birds

Relaxed clock models are fundamental in Bayesian clock dating, but a single distribution characterizing the clock variation is typically selected. Hence, I developed a new reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm for drawing posterior samples between the independent lognormal (ILN) and independent gamma rates (IGR) clock models. The ability of the rjMCMC algorithm to infer the true model was verified through simulations. I then applied the algorithm to the Mesozoic birds data previously analyzed under the white noise (WN) clock model. In comparison, averaging over the ILN and IGR models provided more reliable estimates of the divergence times and evolutionary rates. The ILN model showed slightly better fit than the IGR model and much better fit than the autocorrelated lognormal (ALN) clock model. When the data were partitioned, different partitions showed heterogeneous model fit for ILN and IGR clocks. The implementation provides a general framework for selecting and averaging relaxed clock models in Bayesian dating analyses.

松弛时钟模型（relaxed clock models）是贝叶斯时钟定年（Bayesian clock dating）领域的核心基础，但现有研究通常仅选用单一分布来刻画时钟演化变异。为此，本文开发了一种全新的可逆跳跃马尔可夫链蒙特卡洛（reversible-jump Markov chain Monte Carlo, rjMCMC）算法，用于在独立对数正态（independent lognormal, ILN）与独立伽马速率（independent gamma rates, IGR）时钟模型之间抽取后验样本。通过模拟实验验证了该rjMCMC算法推断真实模型的能力。随后，本文将该算法应用于此前采用白噪声（white noise, WN）时钟模型分析的中生代鸟类数据集。对比结果显示，对ILN与IGR模型进行模型平均，能够得到更为可靠的分化时间与演化速率估计结果。ILN模型的拟合效果略优于IGR模型，且远优于自相关对数正态（autocorrelated lognormal, ALN）时钟模型。当对数据集进行分区处理时，不同分区在ILN与IGR时钟模型下的拟合效果存在异质性。本研究实现的方法框架可为贝叶斯定年分析中松弛时钟模型的选择与模型平均提供通用解决方案。