Mazel Florent, Davies T Jonathan, Georges Damien, Lavergne Sébastien, Thuiller Wilfried, Peres-NetoO Pedro R
Ecology. 2016 Feb;97(2):286-93. doi: 10.1890/15-0086.1.
Phylogenetic Generalized Least Square (PGLS) is the tool of choice among phylogenetic comparative methods to measure the correlation between species features such as morphological and life-history traits or niche characteristics. In its usual form, it assumes that the residual variation follows a homogenous model of evolution across the branches of the phylogenetic tree. Since a homogenous model of evolution is unlikely to be realistic in nature, we explored the robustness of the phylogenetic regression when this assumption is violated. We did so by simulating a set of traits under various heterogeneous models of evolution, and evaluating the statistical performance (type I error [the percentage of tests based on samples that incorrectly rejected a true null hypothesis] and power [the percentage of tests that correctly rejected a false null hypothesis]) of classical phylogenetic regression. We found that PGLS has good power but unacceptable type I error rates. This finding is important since this method has been increasingly used in comparative analyses over the last decade. To address this issue, we propose a simple solution based on transforming the underlying variance-covariance matrix to adjust for model heterogeneity within PGLS. We suggest that heterogeneous rates of evolution might be particularly prevalent in large phylogenetic trees, while most current approaches assume a homogenous rate of evolution. Our analysis demonstrates that overlooking rate heterogeneity can result in inflated type I errors, thus misleading comparative analyses. We show that it is possible to correct for this bias even when the underlying model of evolution is not known a priori.
系统发育广义最小二乘法（PGLS）是系统发育比较方法中用于衡量物种特征（如形态和生活史特征或生态位特征）之间相关性的首选工具。在其通常形式中，它假设残差变异遵循系统发育树各分支上的同质进化模型。由于同质进化模型在自然界中不太可能是现实的，我们探讨了违反这一假设时系统发育回归的稳健性。我们通过在各种异质进化模型下模拟一组性状，并评估经典系统发育回归的统计性能（I型错误[基于样本错误拒绝真零假设的测试百分比]和检验功效[正确拒绝假零假设的测试百分比]）来做到这一点。我们发现PGLS具有良好的功效，但I型错误率不可接受。这一发现很重要，因为在过去十年中，这种方法在比较分析中越来越多地被使用。为了解决这个问题，我们提出了一个简单的解决方案，即基于变换潜在的方差协方差矩阵来调整PGLS中的模型异质性。我们认为，进化速率的异质性在大型系统发育树中可能特别普遍，而目前大多数方法都假设进化速率是同质的。我们的分析表明，忽略速率异质性会导致I型错误膨胀，从而误导比较分析。我们表明，即使事先不知道潜在的进化模型，也有可能纠正这种偏差。
