Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, California 90095-1606 E-mail:
Evolution. 2012 Mar;66(3):752-762. doi: 10.1111/j.1558-5646.2011.01474.x. Epub 2011 Oct 21.
In recent years, a suite of methods has been developed to fit multiple rate models to phylogenetic comparative data. However, most methods have limited utility at broad phylogenetic scales because they typically require complete sampling of both the tree and the associated phenotypic data. Here, we develop and implement a new, tree-based method called MECCA (Modeling Evolution of Continuous Characters using ABC) that uses a hybrid likelihood/approximate Bayesian computation (ABC)-Markov-Chain Monte Carlo approach to simultaneously infer rates of diversification and trait evolution from incompletely sampled phylogenies and trait data. We demonstrate via simulation that MECCA has considerable power to choose among single versus multiple evolutionary rate models, and thus can be used to test hypotheses about changes in the rate of trait evolution across an incomplete tree of life. We finally apply MECCA to an empirical example of body size evolution in carnivores, and show that there is no evidence for an elevated rate of body size evolution in the pinnipeds relative to terrestrial carnivores. ABC approaches can provide a useful alternative set of tools for future macroevolutionary studies where likelihood-dependent approaches are lacking.
近年来，已经开发出了一系列方法来将多个速率模型拟合到系统发育比较数据中。然而，由于这些方法通常需要对树和相关表型数据进行完整的采样，因此它们在广泛的系统发育尺度上的实用性有限。在这里，我们开发并实现了一种新的基于树的方法，称为 MECCA（使用 ABC 模拟连续性状进化的模型），它使用混合似然/近似贝叶斯计算（ABC）-马尔可夫链蒙特卡罗方法，从不完全采样的系统发育树和性状数据中同时推断多样化率和性状进化率。我们通过模拟表明，MECCA 有很大的能力在单一和多个进化率模型之间进行选择，因此可以用于检验关于不完全生命之树中性状进化率变化的假设。最后，我们将 MECCA 应用于食肉动物体尺进化的实证案例，结果表明，相对于陆地食肉动物，鳍足类动物的体尺进化率没有升高的证据。在缺乏似然依赖性方法的未来宏观进化研究中，ABC 方法可以提供一组有用的替代工具。
