Department of Biology, Carleton University, Ottawa, ON, Canada.
Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
Mol Ecol. 2020 Mar;29(5):862-869. doi: 10.1111/mec.15374. Epub 2020 Feb 24.
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.
基于遗传数据划定的群体通常用于野生动物保护和管理。许多研究使用结构程序与 ΔK 法相结合来确定最可能的群体数量(K)。我们最近发现,与未使用 ΔK 法的研究相比,使用 ΔK 法更常确定 K=2。我们提出了两种原因:层次群体结构导致低估,或 ΔK 法未评估 K=1 导致高估。本研究旨在通过在迁移情景下对一个、两个和三个群体进行模拟,更好地了解该方法的局限性。通过这些模拟,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,均值概率图和 ΔK 就无法确定正确的群体数量。我们还发现,ΔK 法强烈倾向于选择 K=2。我们使用这些数据来确定 ΔK 统计量确定未得到充分支持的 K 值的范围。最后,通过模拟和对经验数据的审查,我们发现 ΔK 统计量与群体之间的分歧程度相对应。基于我们的研究结果,我们建议研究人员谨慎使用 ΔK 法;他们需要报告所有相关数据,包括 ΔK 的大小,以及连接性的估计,以便研究社区评估在管理和保护的背景下是否存在有意义的遗传结构。