Rapid assignment of bacterial pathogens into predefined populations is an important first step for epidemiological tracking.
For clonal species, a single allele can theoretically define a population. For non-clonal species such as Burkholderia
pseudomallei, however, shared allelic states between distantly related isolates make it more difficult to identify population
defining characteristics. Two distinct B. pseudomallei populations have been previously identified using multilocus sequence
typing (MLST). These populations correlate with the major foci of endemicity (Australia and Southeast Asia). Here, we use
multiple Bayesian approaches to evaluate the compositional robustness of these populations, and provide assignment
results for MLST sequence types (STs). Our goal was to provide a reference for assigning STs to an established population
without the need for further computational analyses. We also provide allele frequency results for each population to enable
estimation of population assignment even when novel STs are discovered. The ability for humans and potentially
contaminated goods to move rapidly across the globe complicates the task of identifying the source of an infection or
outbreak. Population genetic dynamics of B. pseudomallei are particularly complicated relative to other bacterial pathogens,
but the work here provides the ability for broad scale population assignment. As there is currently no independent
empirical measure of successful population assignment, we provide comprehensive analytical details of our comparisons to
enable the reader to evaluate the robustness of population designations and assignments as they pertain to individual
research questions. Finer scale subdivision and verification of current population compositions will likely be possible with
genotyping data that more comprehensively samples the genome. The approach used here may be valuable for other nonclonal
pathogens that lack simple group-defining genetic characteristics and provides a rapid reference for epidemiologists
wishing to track the origin of infection without the need to compile population data and learn population assignment
algorithms.