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 -