TY - JOUR
T1 - Bayesian analysis of biogeography when the number of areas is large
AU - Landis, Michael J.
AU - Matzke, Nicholas J.
AU - Moore, Brian R.
AU - Huelsenbeck, John P.
N1 - Funding Information:
FUNDING This research was supported by the National Science Foundation (NSF) [DEB 0445453 to J.P.H.; DEB 0842181, DEB 0919529 to B.R.M.] and the National Institutes Health (NIH) [GM-069801 to J.P.H.].
PY - 2013/11
Y1 - 2013/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84886001628&partnerID=8YFLogxK
U2 - 10.1093/sysbio/syt040
DO - 10.1093/sysbio/syt040
M3 - Article
C2 - 23736102
AN - SCOPUS:84886001628
SN - 1063-5157
VL - 62
SP - 789
EP - 804
JO - Systematic Biology
JF - Systematic Biology
IS - 6
ER -