Martins Emília P, Diniz-Filho José Alexandre F, Housworth Elizabeth A
Department of Biology, Indiana University, Bloomington 47408, USA.
Evolution. 2002 Jan;56(1):1-13.
Recently, the utility of modern phylogenetic comparative methods (PCMs) has been questioned because of the seemingly restrictive assumptions required by these methods. Although most comparative analyses involve traits thought to be undergoing natural or sexual selection, most PCMs require an assumption that the traits be evolving by less directed random processes, such as Brownian motion (BM). In this study, we use computer simulation to generate data under more realistic evolutionary scenarios and consider the statistical abilities of a variety of PCMs to estimate correlation coefficients from these data. We found that correlations estimated without taking phylogeny into account were often quite poor and never substantially better than those produced by the other tested methods. In contrast, most PCMs performed quite well even when their assumptions were violated. Felsenstein's independent contrasts (FIC) method gave the best performance in many cases, even when weak constraints had been acting throughout phenotypic evolution. When strong constraints acted in opposition to variance-generating (i.e., BM) forces, however, FIC correlation coefficients were biased in the direction of those BM forces. In most cases, all other PCMs tested (phylogenetic generalized least squares, phylogenetic mixed model, spatial autoregression, and phylogenetic eigenvector regression) yielded good statistical performance, regardless of the details of the evolutionary model used to generate the data. Actual parameter estimates given by different PCMs for each dataset, however, were occasionally very different from one another, suggesting that the choice among them should depend on the types of traits and evolutionary processes being considered.
最近,现代系统发育比较方法(PCMs)的实用性受到了质疑,因为这些方法似乎需要一些限制性假设。尽管大多数比较分析涉及被认为正在经历自然选择或性选择的性状,但大多数PCMs需要一个假设,即性状是通过较少定向的随机过程进化的,比如布朗运动(BM)。在本研究中,我们使用计算机模拟在更现实的进化场景下生成数据,并考虑各种PCMs从这些数据中估计相关系数的统计能力。我们发现,在不考虑系统发育的情况下估计的相关性往往很差,而且从未比其他测试方法得出的相关性有实质性的更好表现。相比之下,即使违反了假设,大多数PCMs的表现仍然相当不错。费尔斯滕森的独立对比(FIC)方法在许多情况下表现最佳,即使在整个表型进化过程中存在较弱的限制。然而,当强限制与产生方差的(即BM)力量相反时,FIC相关系数会朝着那些BM力量的方向产生偏差。在大多数情况下,所有其他测试的PCMs(系统发育广义最小二乘法、系统发育混合模型、空间自回归和系统发育特征向量回归)都产生了良好的统计性能,无论用于生成数据的进化模型的细节如何。然而,不同PCMs为每个数据集给出的实际参数估计偶尔会彼此非常不同,这表明在它们之间的选择应该取决于所考虑的性状类型和进化过程。