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. 理解物种形成速率与灭绝速率在不同支系间以及随时间的变化规律，是检验诸多宏观进化过程相关假说的核心前提。BAMM是一款灵活的贝叶斯框架（Bayesian framework），用于推断系统发育树（phylogenetic trees）中宏观进化速率转移的数量与位置，目前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，针对多样化速率转移的数量指定先验概率分布。但学界对该“模型先验（model prior）”对推断过程的影响知之甚少，其可能同时影响研究者接受比生成模型更复杂（伴随更高错误率）或更简单（检验功效更低）模型的概率。 2. BAMM中的分层泊松过程先验（hierarchical Poisson process prior）可简化为关于速率转移数量的简单几何分布（geometric distribution），我们利用这一特性提升了基于贝叶斯因子（Bayes factors）的模型选择效率。我们使用BAMM v2.5版本，对涵盖宽泛先验参数配置范围、分别设置存在与不存在多样化异质性的模拟系统发育树开展了分析。此外，我们还评估了模型先验对马尔可夫链蒙特卡洛（MCMC）收敛时间以及多样化速率估计结果的影响。 3. 在所有模拟场景中，针对速率转移数量的模型证据（贝叶斯因子支持度）在本次研究考察的宽泛范围内，均不受模型先验选择的影响。当通过贝叶斯因子选择多样化模型时，通过BAMM得到的最优支持模型极少包含虚假转移（仅占所有运行次数的2%以下）。在先验期望跨越三个数量级的情况下，BAMM仍能可靠地推断出真实的多样化速率转移数量。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（预期转移数量更大）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，因为其可在不扭曲速率异质性证据的前提下缩短计算时间。