Bayesian analysis of biogeography when the number of areas is large.

By Michael J. Landis, N. Matzke, Brian R. Moore, John P. Huelsenbeck. Systematic Biology, 2013

788 citations recorded by the source corpus.

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.

Subfields: Ecology, Genetics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/08959d05b4c18cc7f39001759cd16807018c1d3b

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Citations in the corpus

Model selection in historical biogeography reveals that founder-event speciation is a crucial process in Island Clades. (2014, methodology): “These are strange features and ideally should be eliminated either by correcting for exclusion of the null range from the state space (Landis et al. 2013) and/or explicit inclusion of the macroevolutionary birth/death process as in SSE models (e.”
Probabilistic historical biogeography: new models for founder-event speciation, imperfect detection, and fossils allow improved accuracy and model-testing (2013, positive): “ian technique which samples geographical histo‐ ries along phylogenetic branches jointly with the sampling of parameter values (Landis et al. 2013).”
Bayesian estimation of the global biogeographical history of the Solanaceae (2017, methodology): “‘BioGeoBEARS’ implements maximum likelihood (ML) methods that replicate the key assumptions of three most commonly used methods in historical biogeography, namely DEC (dispersal–extinction– cladogenesis; Ree & Smith, 2008), DIVA (dispersal–vicariance analysis; Ronquist, 1997) and BayArea (Bayesian I”
Evaluating the influence of connectivity and distance on biogeographical patterns in the south‐western deserts of North America (2016, methodology): “However, BayArea contains no model for range evolution at cladogenesis, and SHIBA employs a simulation-based approximate Bayesian approach, which is computationally intensive, restricting analyses to a small number of taxa and areas.”
The role of the uplift of the Qinghai‐Tibetan Plateau for the evolution of Tibetan biotas (2015, methodology): “This new approach employs a Bayesian framework and is implemented in BayArea (Landis et al., 2013).”