Subsampling reveals that unbalanced sampling affects STRUCTURE results in a multi-species dataset.

Studying the genetic population structure of species can reveal important insights into several key evolutionary, historical, demographic, and anthropogenic processes. One of the most important statistical tools for inferring genetic clusters is the program STRUCTURE. Recently, several papers have pointed out that STRUCTURE may show a bias when the sampling design is unbalanced, resulting in spurious joining of underrepresented populations and spurious separation of overrepresented populations. Suggestions to overcome this bias include subsampling and changing the ancestry model, but the performance of these two methods has not yet been tested on actual data. Here, I use a data set of 12 high-alpine plant species to test whether unbalanced sampling affects the STRUCTURE inference of population differentiation between the European Alps and the Carpathians. For four of the 12 species, subsampling of the Alpine populations-to match the sample size between the Alps and the Carpathians-resulted in a drastically different clustering than the full data set. On the other hand, STRUCTURE results with the alternative ancestry model were indistinguishable from the results with the default model. Based on these results, the subsampling strategy seems a more viable approach to overcome the bias than the alternative ancestry model. However, subsampling is only possible when there is an a priori expectation of what constitute the main clusters. Though these results do not mean that the use of STRUCTURE should be discarded, it does indicate that users of the software should be cautious about the interpretation of the results when sampling is unbalanced.