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Background  Studying population genetic structure and gene flow of plant populations and their influencing factors is of particular significance in the field of conservation biology, especially important for species such as rare and endangered plants.  Tetraena mongolica  Maxim. (TM), belongs to Zygophyllaceae family, a rare and endangered plant with narrow distribution. However, for the last decade, due to excessive logging, urban expansion, industrial and tourism development, habitat fragmentation and loss of natural habitats have become major threats to the population of endangered plants.    Results  In this study, genetic diversity, population genetic structure and gene flow of TM populations were evaluated by reduced representation sequencing technology, and a total of more than 133.45 GB high-quality clean reads and 38,097 high-quality SNPs were generated. Analysis based on multiple methods, we found that the existing TM populations have moderate levels of genetic diversity, and very low genetic differentiation as well as high levels of gene flow between populations. Population structure and principal coordinates analysis showed that 8 TM populations can be divided into two groups. The Mantel test detected no significant correlation between geographical distances and genetic distance for the whole sampling. Moreover, the migration model indicated that the gene flow is more of a north to south migration pattern in history.    Conclusions  This study demonstrates that the present genetic structure is mainly due to habitat fragmentation caused by urban sprawl, industrial development and coal mining. Our recommendation with respect to conservation management is that, all 8 populations should be preserved as a whole population, rather than just those in the core area of TM nature reserve. In particular, the populations near the edge of TM distribution in cities and industrial areas deserve our special protection.

Population genetic structure and gene flow of rare and endangered Tetraena mongolica Maxim. revealed by reduced representation sequencing