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. 厘清物种形成与灭绝速率的变异模式——既涵盖不同演化支系间的差异，也包含随时间推移的动态变化——对于检验诸多宏观进化（macroevolutionary）过程相关假说具有关键意义。BAMM是一款灵活的贝叶斯框架（Bayesian framework），可用于跨系统发育树（phylogenetic trees）推断宏观进化速率转移（diversification rate shifts）的数量与位置，目前已被广泛应用于各类实证研究中。在开展分析前，BAMM要求研究者预先指定分化速率转移数量的先验概率分布。目前学界对该“模型先验（model prior）”对统计推断的影响尚不明晰，但该影响可能会同时改变研究者接受比生成模型更复杂（伴随高错误率）或更简单（伴随低检验功效）模型的概率。 2. BAMM所采用的分层泊松过程先验（hierarchical Poisson process prior）可简化为速率转移数量上的简单几何分布（geometric distribution），我们借助这一特性提升了基于贝叶斯因子（Bayes factors）的模型选择效率。本研究借助BAMM v2.5版本，针对涵盖大范围先验参数配置的、模拟得到的存在或不存在分化异质性的系统发育树开展了分析。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）收敛时长以及分化速率估计结果的影响。 3. 在所有模拟场景中，针对转移数量的模型证据（贝叶斯因子支持度）在本研究考察的宽范围先验配置下，均未对模型先验的选择表现出敏感性。当采用贝叶斯因子筛选分化模型时，通过BAMM得到的最优支持模型几乎不会包含虚假转移（仅占全部模拟运行次数的2%以下）。在先验预期取值范围跨越3个数量级的各类场景下，BAMM均能稳定准确地推断出分化速率转移的真实数量。但本研究发现，模型先验对MCMC收敛特性存在显著影响：越平坦的先验分布（对应更高的预期转移数量）能够显著提升MCMC模拟的运行效率。 4. 本研究结果支持在BAMM中采用宽松的模型先验，因为该设置能够在不扭曲速率异质性相关证据的前提下，缩短计算时长。