Genomic Data Reveals Population Genetic and Demographic History of Magnolia fistulosa (Magnoliaceae), a Plant Species With Extremely Small Populations in Yunnan Province, China

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Abstract
Elucidating the genetic background of threatened species is fundamental to their management and conservation, and investigating the demographic history of these species is helpful in the determination of the threats facing them. The woody species of the genus Magnolia (Magnoliaceae) have high economic, scientific and ecological values. Although nearly half of all Magnolia species have been evaluated as threatened, to date there has been no population genetic study employing Next Generation Sequencing (NGS) technology in this genus. In the present study, we investigate the conservation genomics of Magnolia fistulosa, a threatened species endemic to the limestone area along the Sino-Vietnamese border, using a double digest restriction-site-associated DNA-sequencing (ddRAD-seq) approach. To increase the reliability of our statistical inferences, we employed two approaches, Stacks and ipyrad, for SNP calling. A total of 15,272 and 18,960, respectively, putatively neutral SNPs were generated by Stacks and ipyrad. Relatively high genetic diversity and large population divergence were detected in M. fistulosa. Although higher absolute values were calculated using the ipyrad data set, the two data sets showed the same trends in genetic diversity (π, He), population differentiation (FST) and inbreeding coefficients (FIS). A change in the effective population size of M. fistulosa within the last 1 Ma was detected, including a population decline about 0.5–0.8 Ma ago, a bottleneck event about 0.2–0.3 Ma ago, population fluctuations during the last glacial stage, and the recovery of effective population size after the last glacial maximum. Our findings not only lay the foundation for the future conservation of this species, but also provide new insights into the evolutionary history of the genus Magnolia in southeastern Yunnan, China.
Funding Information
  • National Natural Science Foundation of China
  • National Natural Science Foundation of China-Yunnan Joint Fund

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