An eigenvector method for estimating phylogenetic inertia
An eigenvector method for estimating phylogenetic inertia
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DOI：
10.1111/j.1558-5646.1998.tb02006.x
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发表时间：
1998-10-01
期刊：
影响因子：
3.3
通讯作者：
Bini, LM
中科院分区：
文献类型：
--
作者：
Diniz, JAF;De Sant'ana, CER;Bini, LM
我们提出了一种在比较数据分析中估计和修正系统发育惯量的新方法。这种方法被称为系统发育特征向量回归(PVR)，首先对物种之间的成对系统发育距离矩阵进行主坐标分析。被分析的性状被回归到由折杆模型保留的特征向量上，以这种方式，估计值表示数据中的系统发育趋势，残差表示每个物种的独立进化。这种划分类似于空间自回归方法实现的划分，但克服了自回归方法在系统发育相关性低或样本量太小而无法检测到系统发育相关性时统计性能较低的问题。此外，PVR更容易在大样本下执行，因为它基于众所周知的多变量和回归分析技术。我们使用真实数据集和仿真对PVR的性能进行了评估，并与自回归方法进行了比较。一个使用食肉目哺乳动物体型进化的详细工作例子表明，这一特征的系统发育惯性被提高，并且两种方法估计的结果相似。在这个例子中，在Pvr的α=0.05时的类型I误差等于0.048，但是在回归中使用的特征向量的数目的增加增加了误差。此外，PVR和自回归方法之间的相似性由它们的残差之间的相关性定义，通过高估表示系统发育距离矩阵所需的特征值的数量而降低。为了评价分支图拓扑结构对从双中心系统发育距离矩阵中提取的特征值分布的影响，我们分析了100个随机生成的分支图(多达100个种)。对数变换变量的多元线性回归表明，折枝模型提取的特征值个数可以完全用分支图拓扑来解释。因此，折杆模型是确定PVR要使用的特征向量的正确数目的适当标准。我们还模拟了不同水平的系统发育惯性，方法是在10个、25个和50个物种之间产生一个趋势，并在这个趋势周围添加残差增加的随机向量。在这样做的过程中，我们使用与之前测试的不同进化模型生成的数据来评估这两种方法的性能。结果表明，当样本量较大(大于25种)和系统发育惯量较大时，PVR和自回归方法都能有效地检测数据中的惯量。然而，PVR在样本量较小和系统发育惯量较低的情况下更有效。这些结论也得到了对10个真实数据集的分析，这些数据集是关于不同动物分支中身体尺寸演变的。我们得出结论，在比较数据分析中，PVR可以作为自回归方法的一个有用的替代方法。
We propose a new method to estimate and correct for phylogenetic inertia in comparative data analysis. The method, called phylogenetic eigenvector regression (PVR) starts by performing a principal coordinate analysis on a pairwise phylogenetic distance matrix between species. Traits under analysis are regressed on eigenvectors retained by a broken-stick model in such a way that estimated values express phylogenetic trends in data and residuals express independent evolution of each species. This partitioning is similar to that realized by the spatial autoregressive method, but the method proposed here overcomes the problem of low statistical performance that occurs with autoregressive method when phylogenetic correlation is low or when sample size is too small to detect it. Also, PVR is easier to perform with large samples because it is based on well-known techniques of multivariate and regression analyses. We evaluated the performance of PVR and compared it with the autoregressive method using real datasets and simulations. A detailed worked example using body size evolution of Carnivora mammals indicated that phylogenetic inertia in this trait is elevated and similarly estimated by both methods. In this example, Type I error at alpha = 0.05 of PVR was equal to 0.048, but an increase in the number of eigenvectors used in the regression increases the error. Also, similarity between PVR and the autoregressive method, defined by correlation between their residuals, decreased by overestimating the number of eigenvalues necessary to express the phylogenetic distance matrix. To evaluate the influence of cladogram topology on the distribution of eigenvalues extracted from the double-centered phylogenetic distance matrix, we analyzed 100 randomly generated cladograms (up Ito 100 species). Multiple linear regression of log transformed variables indicated that the number of eigenvalues extracted by the broken-stick model can be fully explained by cladogram topology. Therefore, the broken-stick model is an adequate criterion for determining the correct number of eigenvectors to be used by PVR. We also simulated distinct levels of phylogenetic inertia by producing a trend across 10, 25, and 50 species arranged in "comblike" cladograms and then adding random vectors with increased residual variances around this trend. In doing so, we provide an evaluation of the performance of both methods with data generated under different evolutionary models than tested previously. The results showed that both PVR and autoregressive method are efficient in detecting inertia in data when sample size is relatively high (more than 25 species) and when phylogenetic inertia is high. However, PVR is more efficient at smaller sample sizes and when level of phylogenetic inertia is low. These conclusions were also supported by the analysis of 10 real datasets regarding body size evolution in different animal clades. We concluded that PVR can be a useful alternative to an autoregressive method in comparative data analysis.