Department of Integrative Biology, University of California, Berkeley, CA 94720-3140, USA; Department of Evolution and Ecology, University of California, Davis, Storer Hall, One Shields Avenue, Davis, CA 95616, USA; and Biology Department, King Abdulaziz University, Jeddah, Saudi Arabia.
Syst Biol. 2013 Nov;62(6):789-804. doi: 10.1093/sysbio/syt040. Epub 2013 Jun 4.
Historical biogeography is increasingly studied from an explicitly statistical perspective, using stochastic models to describe the evolution of species range as a continuous-time Markov process of dispersal between and extinction within a set of discrete geographic areas. The main constraint of these methods is the computational limit on the number of areas that can be specified. We propose a Bayesian approach for inferring biogeographic history that extends the application of biogeographic models to the analysis of more realistic problems that involve a large number of areas. Our solution is based on a "data-augmentation" approach, in which we first populate the tree with a history of biogeographic events that is consistent with the observed species ranges at the tips of the tree. We then calculate the likelihood of a given history by adopting a mechanistic interpretation of the instantaneous-rate matrix, which specifies both the exponential waiting times between biogeographic events and the relative probabilities of each biogeographic change. We develop this approach in a Bayesian framework, marginalizing over all possible biogeographic histories using Markov chain Monte Carlo (MCMC). Besides dramatically increasing the number of areas that can be accommodated in a biogeographic analysis, our method allows the parameters of a given biogeographic model to be estimated and different biogeographic models to be objectively compared. Our approach is implemented in the program, BayArea.
历史生物地理学越来越多地从明确的统计角度进行研究，使用随机模型来描述物种范围的演化，将其视为离散地理区域之间扩散和灭绝的连续时间马尔可夫过程。这些方法的主要限制是可以指定的区域数量的计算限制。我们提出了一种贝叶斯方法来推断生物地理历史，该方法将生物地理模型的应用扩展到分析涉及大量区域的更现实的问题。我们的解决方案基于“数据增强”方法，首先在树中填充与树末端的观察到的物种范围一致的生物地理事件历史。然后，我们通过采用瞬时率矩阵的机械解释来计算给定历史的可能性，该矩阵指定了生物地理事件之间的指数等待时间和每种生物地理变化的相对概率。我们在贝叶斯框架中开发了这种方法，通过马尔可夫链蒙特卡罗（MCMC）对所有可能的生物地理历史进行边缘化。除了极大地增加生物地理分析中可以容纳的区域数量外，我们的方法还允许估计给定生物地理模型的参数，并客观地比较不同的生物地理模型。我们的方法在程序 BayArea 中实现。
