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Geographic isolation and climatic heterogeneity drive population differentiation of Rosa chinensis var. spontanea complex.

S Q LiC ZhangXin-Fen Gao
Published in: Plant biology (Stuttgart, Germany) (2023)
Global biodiversity is contracting rapidly due to potent anthropogenic activities and severe climate change. Wild populations of Rosa chinensis var. spontanea and Rosa lucidissima are rare species endemic to China and also important germplasm resources for rose breeding. However, these populations are at acute risk of extinction and require urgent action to ensure their preservation. We harnessed 16 microsatellite loci to 44 populations of these species and analyzed population structure and differentiation, demographic history, gene flow and barrier effect. In addition, niche overlap test and potential distribution modelling in different periods were also carried out. The data indicate that: (1) R. lucidissima cannot be regarded as a separate species from R. chinensis var. spontanea; (2) the Yangtze River and the Wujiang River function as barriers in population structure and differentiation, and precipitation in coldest quarter may be the key factor for niche divergence of R. chinensis var. spontanea complex; (3) historical gene flow showed a converse tendency to current gene flow, indicating that alternate migration events of R. chinensis var. spontanea complex between south and north were a response to climate oscillations; and, (4) extreme climate change will decrease the distribution range of R. chinensis var. spontanea complex whereas the opposite occurs under moderate scenario in the future. Our results disentangle the relationship between R. chinensis var. spontanea and R. lucidissima, highlight the pivotal roles of geographic isolation and climate heterogeneity in their population differentiation and provide an important reference for comparable conservation studies on other endangered species.
Keyphrases
  • climate change
  • genetic diversity
  • genome wide
  • human health
  • copy number
  • single cell
  • working memory
  • big data
  • gene expression
  • dna methylation
  • intensive care unit
  • machine learning
  • drug induced