Data from: Bayesian model selection with BAMM: effects of the model prior on the inferred number of diversification shifts

1. Understanding variation in rates of speciation and extinction -- both among lineages and through time -- is critical to the testing of many hypotheses about macroevolutionary processes. BAMM is a flexible Bayesian framework for inferring the number and location of shifts in macroevolutionary rate across phylogenetic trees and has been widely used in empirical studies. BAMM requires that researchers specify a prior probability distribution on the number of diversification rate shifts before conducting an analysis. The consequences of this "model prior" for inference are poorly known but could potentially influence both the probability of accepting models that are more (high error rate) or less (low power) complex than the generating model. 2. The hierarchical Poisson process prior in BAMM reduces to a simple geometric distribution on number of rate shifts and we use this property to increase the efficiency of model selection with Bayes factors. Using BAMM v2.5, we analyzed phylogenies simulated with and without diversification heterogeneity across a broad range of prior parameterizations. We also assessed the impact of the model prior on MCMC convergence times and on diversification rate estimates. 3. For all simulation scenarios, model evidence (Bayes factor support) for the number of shifts is not sensitive to the choice of model prior over the wide range examined here. The best-supported model found using BAMM rarely includes spurious shifts (<2% of all runs) when diversification models are selected using Bayes factors. BAMM was reliably able to infer the true number of diversification rate shifts across prior expectations that varied by three orders of magnitude. However, we find a strong effect of model prior on MCMC convergence properties: a flatter prior distribution (larger expected number of shifts) can dramatically increase the efficiency of the MCMC simulation. 4. Our results support the use of a liberal model prior in BAMM, as it reduces computation time without distorting the evidence for rate heterogeneity.1. 解析物种形成与灭绝速率的谱系间差异及随时间的变化，对检验诸多宏观进化过程相关假说至关重要。BAMM是一款灵活的贝叶斯框架，用于推断跨系统发育树的宏观进化速率转移的数量与位置，已在实证研究中得到广泛应用。BAMM要求研究者在开展分析前，先指定多样化速率转移数量的先验概率分布。目前人们对该"模型先验"在统计推断中的影响知之甚少，但它可能会影响接受比生成模型更复杂（高错误率）或更简单（低检验功效）模型的概率。 2. BAMM中的分层泊松过程先验可简化为速率转移数量的简单几何分布，我们利用这一特性提升了基于贝叶斯因子的模型选择效率。我们使用BAMM v2.5，在广泛的先验参数配置范围内，对存在和不存在多样化异质性的模拟系统发育树进行了分析。此外，我们还评估了模型先验对马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）收敛时间及多样化速率估计值的影响。 3. 在所有模拟场景中，本次研究所考察的宽范围模型先验选择下，用于推断转移数量的模型证据（贝叶斯因子支持度）均不敏感。当通过贝叶斯因子选择多样化模型时，BAMM识别的最优支持模型极少包含虚假转移（占所有运行结果的比例低于2%）。在先验期望跨越三个数量级变化的情况下，BAMM仍能可靠地推断出多样化速率转移的真实数量。不过我们发现，模型先验对MCMC收敛特性存在显著影响：更平缓的先验分布（期望转移数量更大）可显著提升MCMC模拟的效率。 4. 本研究结果支持在BAMM中使用宽松的模型先验，因为它可在不扭曲速率异质性证据的前提下缩短计算时长。