Bayesian inference also proffers particular benefits within the class of likelihood-based methods, for example, by allowing for straightforward approaches to control model complexity. BSSVS naturally provides a BF test to identify significant non-zero migration rates. Further prior specification easily incorporates geographical detail of the sequence data. Although distanceinformed priors appear to have little impact on the phylogeographic analyses presented here, both BSSVS and informed priors furnish new opportunities for hypothesis testing when comparing competing prior scenarios of the diffusion process. Examples include ''gravity models'' [41] in which infinitesimal rates become functions of the host population-sizes at the end-point locations and a priori structurally-fixed graphs [19] . Finally, it has been recognized that an MCMC-based Bayesian framework is wellsuited to bring together information of different kinds [42] . The BEAST software, which has a strong focus on calibrated phylogenies and genealogies, elegantly illustrates this by offering a large number of complementary evolutionary models including substitution models, demographic and relaxed clock models that can be combined into a full probabilistic model [43] . By adding spatial reconstruction to this arsenal of evolutionary models, the full probabilistic inference now brings us much closer to biogeographical history reconstruction from genetic data.