A New Method for Handling Missing Species in Diversification Analysis Applicable to Randomly or Nonrandomly Sampled Phylogenies
A New Method for Handling Missing Species in Diversification Analysis Applicable to Randomly or Nonrandomly Sampled Phylogenies
复制标题
DOI：
10.1093/sysbio/sys031
复制
发表时间：
2012-10-01
影响因子：
6.5
通讯作者：
Renner, Susanne S.
中科院分区：
文献类型：
--
作者：
Cusimano, Natalie;Stadler, Tanja;Renner, Susanne S.
分子年代测定的时间表越来越多地被用来推断多样化的速率及其随时间的变化。这种分析的一个主要限制是不完整的物种抽样，而且通常是非随机的。虽然广泛使用的伽马统计量与蒙特卡洛恒定速率检验或Delta AICrc检验统计量的出生死亡似然分析适合于比较随机物种抽样下不同系统发育多样化模型的拟合，但尚未开发出客观的自动化方法来拟合非随机抽样的系统发育多样化模型。在这里，我们引入了一种新颖的方法，CorSiM，它涉及在恒定速率的出生死亡模型下模拟缺失的分裂，并允许用户指定正在分析的系统发育中的物种采样是随机的还是非随机的。完成的树可用于后续的模型拟合分析。这与以前的多样化率估计方法有根本的不同，以前的多样化率估计方法是基于不完全树衍生的零分布。CorSiM在R包中是自动化的，可以很容易地应用于大型数据集。我们在两个天南星科分支中说明了这种方法，一个是随机抽样的52%，另一个是非随机抽样的55%。在后一种进化中，CorSiM方法检测和量化多样化率的增加，而经典方法更倾向于恒定速率模型；在前一个分支中，不同方法的结果没有差异（正如预期的那样，因为经典方法只对随机抽样的系统发育有效）。CorSiM方法大大减少了多样化分析中的1类误差，但2类误差仍然是一个方法学问题。
Chronograms from molecular dating are increasingly being used to infer rates of diversification and their change over time. A major limitation in such analyses is incomplete species sampling that moreover is usually nonrandom. While the widely used gamma statistic with the Monte Carlo constant-rates test or the birth death likelihood analysis with the Delta AICrc test statistic are appropriate for comparing the fit of different diversification models in phylogenies with random species sampling, no objective automated method has been developed for fitting diversification models to nonrandomly sampled phylogenies. Here, we introduce a novel approach, CorSiM, which involves simulating missing splits under a constant rate birth death model and allows the user to specify whether species sampling in the phylogeny being analyzed is random or nonrandom. The completed trees can be used in subsequent model-fitting analyses. This is fundamentally different from previous diversification rate estimation methods, which were based on null distributions derived from the incomplete trees. CorSiM is automated in an R package and can easily be applied to large data sets. We illustrate the approach in two Araceae clades, one with a random species sampling of 52% and one with a nonrandom sampling of 55%. In the latter clade, the CorSiM approach detects and quantifies an increase in diversification rate, whereas classic approaches prefer a constant rate model; in the former clade, results do not differ among methods (as indeed expected since the classic approaches are valid only for randomly sampled phylogenies). The CorSiM method greatly reduces the type 1 error in diversification analysis, but type II error remains a methodological problem.