Populations delineated based on genetic data are commonly used for wildlife conservation and management. Many studies use the program structure combined with the Δ
K method to identify the most probable number of populations (
K). We recently found
K = 2 was identified more often when studies used Δ
K compared to studies that did not. We suggested two reasons for this: hierarchical population structure leads to underestimation, or the Δ
K method does not evaluate
K = 1 causing an overestimation. The present contribution aims to develop a better understanding of the limits of the method using one, two and three population simulations across migration scenarios. From these simulations we identified the “best
K” using model likelihood and Δ
K. Our findings show that mean probability plots and Δ
K are unable to resolve the correct number of populations once migration rate exceeds 0.005. We also found a strong bias towards selecting
K = 2 using the Δ
K method. We used these data to identify the range of values where the Δ
K statistic identifies a value of
K that is not well supported. Finally, using the simulations and a review of empirical data, we found that the magnitude of Δ
K corresponds to the level of divergence between populations. Based on our findings, we suggest researchers should use the Δ
K method cautiously; they need to report all relevant data, including the magnitude of Δ
K, and an estimate of connectivity for the research community to assess whether meaningful genetic structure exists within the context of management and conservation.
相似文献