Populations delineated based on genetic data are commonly used for wildlife conservation and management. Many studies use the program structure combined with the ΔK method to identify the most probable number of populations (K). We recently found K = 2 was identified more often when studies used ΔK compared to studies that did not. We suggested two reasons for this: hierarchical population structure leads to underestimation, or the ΔK 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 ΔK. Our findings show that mean probability plots and ΔK 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 ΔK method. We used these data to identify the range of values where the ΔK 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 ΔK corresponds to the level of divergence between populations. Based on our findings, we suggest researchers should use the ΔK method cautiously; they need to report all relevant data, including the magnitude of ΔK, and an estimate of connectivity for the research community to assess whether meaningful genetic structure exists within the context of management and conservation.

中文翻译：

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使用ΔK方法自信地确定正确的K值：什么时候K = 2？

根据遗传数据划定的种群通常用于野生动植物的保护和管理。许多研究使用程序结构结合ΔK方法来确定最可能的人口数（K）。我们最近发现，与没有 使用ΔK的研究相比，使用ΔK的研究更经常识别K = 2 。我们提出以下两个原因：层次总体结构导致低估，或者ΔK方法不评估K = 1导致高估。本文稿旨在通过跨迁移场景的一，二和三人口模拟更好地理解该方法的局限性。从这些模拟中，我们使用模型似然和ΔK确定了“最佳K ” 。我们的研究结果表明，一旦迁移率超过0.005 ，平均概率图和ΔK将无法解析正确的种群数量。我们还发现了 使用ΔK方法选择K = 2的强烈偏见。我们使用这些数据来确定值范围，其中ΔK统计量确定K值没有很好的支持。最后，通过模拟和对经验数据的回顾，我们发现ΔK的大小对应于总体之间的差异水平。根据我们的发现，我们建议研究人员应谨慎使用ΔK方法。他们需要报告所有相关数据，包括ΔK的大小，以及对研究社区的连通性进行评估，以评估在管理和保护范围内是否存在有意义的遗传结构。