Despite different time scales for HA and NA, most probable location states agree on Guangdong as the predominant location of these sequences throughout the majority of their evolutionary history. As an indication of the A-H5N1 epidemic origin, we consider the inferred location at the root of the trees (Figure 1 ). In the HA tree, Guangdong and Hong Kong share a vast majority of the posterior mass, neighboring locations in which surveillance efforts report early Avian influenza cases [33, 34] . In the NA tree, although Hong Kong and Guangdong still obtain marginally higher support than other locations, all posterior root state probabilities are much closer to their prior probability. The substantially deeper NA root explains this difference as the depth greatly increases uncertainty on the root state. Table 1 quantifies differences in ancestral state reconstruction uncertainty between the HA and NA trees using the Kullback-Leibler (KL) divergence measure (see Methods). The NA tree results in considerably lower KL divergence than the HA tree, signifying a much smaller deviation of the posterior distribution of the root location from the prior. However, lack of phylogeographic structure in the data does not contribute to this difference because the NA trees return a lower association index (AI). This measure of spatial admixture is based on a sum across all nodes in the tree of the complement of the frequency of the most abundant location among all descendent taxa weighted by the depth of the node in the tree [35] , and thus bears some relationship with an entropy value for descendent taxa locations. The AI rescales this sum by its expectation for randomized location assignments and results in low values for relatively strong phylogeny-locality correlation whereas AI values close to one reflect complete spatial admixture. If the basal Hong Kong clade and a chicken strain from Hebei have indeed acquired a different NA through reassortment, the root state might be difficult to interpret for NA and is not necessarily the same as that for HA. Therefore, we also list uncertainty measures for the marginal posterior distribution of the most recent common ancestor (MRCA) of the Gs/GD lineage, named after the A/ goose/Guangdong/1/96 strain very close to this node (indicated in Figure 1 ). KL divergence is again lower for this node in the NA phylogeny, but the difference is not as pronounced as for the root node. Table 1 also explores the effects of distance-informed priors and BSSVS on location reconstruction. In general, the distance- Figure 1 . Maximum clade credibility (MCC) phylogenies for hemagglutinin (HA) and neuraminidase (NA) genes of Avian influenza A-H5N1. We color branches according to the most probable location state of their descendent nodes. We use the same color coding as [22] . To the upper left of both phylogenies are their root location state posterior probability distributions. A white arrow indicates the A/goose/Guangdong/1/96 sequence; a filled white circle identifies the most recent common ancestor of the Gs/GD lineage named after this strain. doi:10.1371/journal.pcbi.1000520.g001 informed priors furnish little advantage while inferring the root locations for both the HA and NA trees. If anything, KL divergences are slightly smaller for models involving distanceinformed priors than those with flat priors. For these data, this finding is unsurprising as physical distances can be poor proxies for inverse-diffusion rates when dispersal results from a heterogenous mix of migratory birds, transport of poultry and poultry products, and trade of wild birds [36] . Finally, we also investigated the uncertainty that is accommodated by averaging over plausible trees by analyzing the HA data using a fixed tree topology and branch lengths ( Table 1 ). The state reconstructions for the Gs/GD node in the fixed tree topology appear to ignore some uncertainty in comparison to integrating trees, which is not that evident for the root node. Although state reconstruction uncertainty is expected to be correlated among nodes, we also compared the KL divergence summed over all internal nodes, indicating much higher KL divergences using a fixed tree topology, e.g. for HA, m jk~C : 292 vs 523 for integrating trees and a fixed tree respectively.