Bayesian Analysis of Biogeography when the Number of Areas is Large
Bayesian Analysis of Biogeography when the Number of Areas is Large
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DOI：
10.1093/sysbio/syt040
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发表时间：
2013-11-01
影响因子：
6.5
通讯作者：
Huelsenbeck, John P.
中科院分区：
文献类型：
--
作者：
Landis, Michael J.;Matzke, Nicholas J.;Huelsenbeck, John P.
历史生物地理学越来越多地从明确的统计学角度进行研究，使用随机模型将物种范围的演化描述为一组离散地理区域内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对可以指定的区域数量的计算限制。我们提出了一种推断生物地理历史的贝叶斯方法，将生物地理模型的应用扩展到分析涉及大量领域的更现实的问题。我们的解决方案基于一种“数据增强”的方法，即我们首先在树上填充与树顶端观察到的物种范围一致的生物地理事件的历史。然后，我们通过采用瞬时速率矩阵的机械解释来计算给定历史的可能性，该瞬时速率矩阵指定了生物地理事件之间的指数等待时间和每个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，使用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际化。除了显著增加生物地理分析中可容纳的区域数量外，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BayArea项目中得到了实现。[祖传地区分析；贝叶斯生物地理推断；数据扩充；历史生物地理学；马尔科夫链蒙特卡罗。]
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. [ancestral area analysis; Bayesian biogeographic inference; data augmentation; historical biogeography; Markov chain Monte Carlo.].