Characterizing genetic diversity and structure and identifying conservation units are both crucial for the conservation and management of threatened species. The development of high-throughput sequencing technology provides exciting opportunities for conservation genetics. Here, we employed the powerful SuperGBS method to identify 33, 758 high-quality single-nucleotide polymorphisms (SNP) from 134 individuals of a critically endangered montane shrub endemic to North China, Lonicera oblata. A low level of genetic diversity and a high degree of genetic differentiation among populations were observed based on the SNP data. Both principal component and phylogenetic analyses detected seven clusters, which correspond exactly to the seven geographic populations. Under the optimal K = 7, Admixture suggested the combination of the two small and geographically neighboring populations in the Taihang Mountains, Dongling Mountains, and Lijiazhuang, while the division of the big population of Jiankou Great Wall in the Yan Mountains into two clusters. High population genetic diversity and a large number of private alleles were detected in the four large populations, while low diversity and non-private alleles were observed for the remaining three small populations, implying the importance of these large populations as conservation units in priority. Demographic history inference suggested two drastic contractions of population size events that occurred after the Middle Pleistocene Transition and the Last Glacial Maximum, respectively. Combining our previous ecological niche modeling results with the present genomic data, there was a possible presence of glacial refugia in the Taihang and Yan Mountains, North China. This study provides valuable data for the conservation and management of L. oblata and broadens the understanding of the high biodiversity in the Taihang and Yan Mountains.
Keywords: Lonicera oblata; Taihang Mountains; Yan Mountains; conservation units; genetic diversity; glacial refugia; threatened species.
Copyright © 2022 Mu, Wu, Shen, Tong, Lei, Xia and Ning.