Document Details Document Type : Article In Journal  Document Title : Bayesian Analysis of Biogeography when the Number of Areas is Large Bayesian Analysis of Biogeography when the Number of Areas is Large   Document Language : English  Abstract : 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.].  ISSN : 1063-5157  Journal Name : SYSTEMATIC BIOLOGY  Volume : 62  Issue Number : 6  Publishing Year : 1434 AH 2013 AD  Article Type : Article  Added Date : Tuesday, July 25, 2017  Researchers Researcher Name (Arabic) Researcher Name (English) Researcher Type Dr Grade Email Michael Landis Landis, Michael Researcher     Nicholas Matzke Matzke, Nicholas Researcher     Brian Moore Moore, Brian Researcher     John Huelsenbeck Huelsenbeck, John Researcher     Files File Name Type Description  42261.pdf pdf   Back To Researches Page