Data from: The program STRUCTURE does not reliably recover the correct population structure when sampling is uneven: sub-sampling and new estimators alleviate the problem

Inferences of population structure and more precisely the identification
of genetically homogeneous groups of individuals are essential to the
fields of ecology, evolutionary biology, and conservation biology. Such
population structure inferences are routinely investigated via the program
STRUCTURE implementing a Bayesian algorithm to identify groups of
individuals at Hardy-Weinberg and linkage equilibrium. While the method is
performing relatively well under various population models with even
sampling between subpopulations, the robustness of the method to uneven
sample size between subpopulations and/or hierarchical levels of
population structure has not yet been tested despite being commonly
encountered in empirical datasets. In this study, I used simulated and
empirical microsatellite datasets to investigate the impact of uneven
sample size between subpopulations and/or hierarchical levels of
population structure on the detected population structure. The results
demonstrated that uneven sampling often leads to wrong inferences on
hierarchical structure and downward biased estimates of the true number of
subpopulations. Distinct subpopulations with reduced sampling tended to be
merged together, whilst at the same time, individuals from extensively
sampled subpopulations were generally split, despite belonging to the same
panmictic population. Four new supervised methods to detect the number of
clusters were developed and tested as part of this study and were found to
outperform the existing methods using both evenly and unevenly sampled
datasets. Additionally, a sub-sampling strategy aiming to reduce sampling
unevenness between subpopulations is presented and tested. These results
altogether demonstrate that when sampling evenness is accounted for, the
detection of the correct population structure is greatly improved.