Realistic scenarios of missing taxa in phylogenetic comparative methods and their effects on model selection and parameter estimation
Realistic scenarios of missing taxa in phylogenetic comparative methods and their effects on model selection and parameter estimation
复制标题
系统发育比较方法中缺失类群的现实情景及其对模型选择和参数估计的影响
作者：
Rafael S. Marcondes
基于模型的连续性状进化分析使丰富的进化洞察力。这些分析需要一个系统发育树和树的终端类群的性状值的向量，但很少做一个树和数据集包括所有类群内的一个分支。因为一个分类单元被包含在数据集中的概率取决于具有系统发育信号的生态性状，所以在真实的数据集中缺失的分类单元应该被预期为在遗传学上聚集或与建模的性状相关。我研究了这些类型的缺失类群是否代表模型选择和参数估计的问题。我在一套布朗运动和Ornstein-Uhlenbeck模型下模拟了单变量性状，并评估了模型选择和参数估计在缺失、随机、聚集或相关缺失分类群下的性能。我发现，这些分析在几乎所有情况下都表现良好，包括样本非常稀疏的情况。我检测到的唯一显著偏差是在相关缺失分类群的百分比非常高（90%）的情况下的参数估计。我的研究结果提供了一定程度的保证连续性状进化与缺失类群的研究，但在系统发育比较方法中的缺失类群的问题仍然需要进一步的调查。我在此描述的框架可以为今后的工作提供一个起点。
Model-based analyses of continuous trait evolution enable rich evolutionary insight. These analyses require a phylogenetic tree and a vector of trait values for the tree’s terminal taxa, but rarely do a tree and dataset include all taxa within a clade. Because the probability that a taxon is included in a dataset depends on ecological traits that have phylogenetic signal, missing taxa in real datasets should be expected to be phylogenetically clumped or correlated to the modelled trait. I examined whether those types of missing taxa represent a problem for model selection and parameter estimation. I simulated univariate traits under a suite of Brownian Motion and Ornstein-Uhlenbeck models, and assessed the performance of model selection and parameter estimation under absent, random, clumped or correlated missing taxa. I found that those analyses perform well under almost all scenarios, including situations with very sparsely sampled phylogenies. The only notable biases I detected were in parameter estimation under a very high percentage (90%) of correlated missing taxa. My results offer a degree of reassurance for studies of continuous trait evolution with missing taxa, but the problem of missing taxa in phylogenetic comparative methods still demands much further investigation. The framework I have described here might provide a starting point for future work.
影响因子：
64.8
作者：
Jetz, W.;Thomas, G. H.;Mooers, A. O.
通讯作者：
Mooers, A. O.