%0 Journal Article %1 landis2013bayesian %A Landis, M J %A Matzke, N J %A Moore, B R %A Huelsenbeck, J P %D 2013 %J Syst Biol %K ancestral_range_inference context-dependent-mutation phylogenetics phylogeography %R 10.1093/sysbio/syt040 %T Bayesian Analysis of Biogeography when the Number of Areas is Large %U http://www.ncbi.nlm.nih.gov/pubmed/23736102 %X 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..

@article{landis2013bayesian, 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.].}, added-at = {2013-10-17T19:32:21.000+0200}, author = {Landis, M J and Matzke, N J and Moore, B R and Huelsenbeck, J P}, biburl = {https://www.bibsonomy.org/bibtex/2fea50cab25f8ec6ffafa49edf68932e2/peter.ralph}, doi = {10.1093/sysbio/syt040}, interhash = {605b042cec0c4bc65516584cbcada66b}, intrahash = {fea50cab25f8ec6ffafa49edf68932e2}, journal = {Syst Biol}, keywords = {ancestral_range_inference context-dependent-mutation phylogenetics phylogeography}, month = jul, pmid = {23736102}, timestamp = {2013-10-17T19:32:21.000+0200}, title = {Bayesian Analysis of Biogeography when the Number of Areas is Large}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23736102}, year = 2013 }