Our primary motivation for exploiting BSSVS to select among all possible migration graphs is to elucidate the limited number of epidemiological links that appropriately explain the viral diffusion process. This parsimonious set both informs major modes of migration and reduces the high statistical variance that burdens estimation of all pairwise transition rates. Following this argument, less uncertainty on node state reconstructions would be expected when focusing on a parsimonious parameterization of the instantaneous rate matrix. The A-H5N1 analysis indeed indicates lower uncertainty of root state reconstructions. However, for some other internal nodes, we note the opposite behavior. We attribute this to the reversibility assumption in the rate matrix. Selection of reversible rates by BSSVS imposes more balanced transitions in the phylogeny among locations that could have unidirectional links in reality. Therefore, work is in progress to develop non-reversible models that may better fit a spatially expanding epidemic like A-H5N1 or recurring epidemic influenza emergence through sourcesink dynamics [2] . Considerable technical hurdles remain to incorporate BSSVS procedures under such models. Because BSSVS places non-negligible probability on structural zeros in the rate matrix, we can not guarantee that all resulting rate matrices are diagonalizable, challenging stable computation. Bearing in mind the reversibility assumption, we pass no judgement on the origin of the A-H5N1 epidemic based on the frequency by which a location is present in well-supported rates, as was previously done in the parsimony analysis [22] . Instead, we focus on node location state reconstructions throughout the phylogeny and their posterior probabilities. Figure 1 suggests that, although Hong Kong and Guangdong both receive posterior support as root location states, the dominant location throughout the phylogeny and hence the more likely hub of diffusion is Guangdong. An inherent assumption of the discrete model of location change is that ancestral viruses necessarily reside at only the sampled locations of the extant viruses. In this respect, it is important to realize that the CTMC process should describe the underlying spatial dynamics more accurately as the sampling density increases. E.g., for A-H5N1 [26] , provide more recently obtained sequence data across a larger set of geographic locations; the data could inform further pathways seeding remote localities that remain elusive in our present analysis. In addition to tackling the reversibility assumption, it may also prove necessary to relax constant diffusion rates through time to realistically model phylogeographic processes in many situations. Covarion-like models [44] and allowing different diffusion matrices across different time intervals in the phylogeny may help achieve these aims.