Local adaptation and demographic history of vulnerable conifer Cephalotaxus oliveri in southern China
Having a comprehensive understanding of genetic differentiation, responses to environmental change and demographic history is critical for genetic improvement and conservation efforts. Forest trees are an excellent resource for understanding population differentiation and adaptive genetic variation due to their ability to adapt to different climates and environments. Cephalotaxus oliveri is a relict conifer endemic to China. In this study, we generated transcriptome data and identified 17 728 high-quality single-nucleotide polymorphisms (SNPs) from 18 populations. We found significant negative correlations between expression diversity and nucleotide diversity within and among populations, suggesting that gene expression and nucleotide diversity have a reciprocal relationship when the species adapts to the environment. The analyses of population structure showed that C. oliveri displayed a striking genetic structure with four groups. BayeScEnv and RDA methods detected the signatures of local adaptation, and identified that 738 outlier SNPs were associated with precipitation, temperature and soil conditions across heterogeneous environmental conditions. Approximate Bayesian computation analyses showed that the first and second divergence occurred in the late Miocene (c. 10.075 million years ago [Ma]) and the middle Pleistocene transition (c. 0.815 Ma), respectively. Ecological niche modeling of C. oliveri revealed signs of westward expansion after the last glacial maximum, while it was predicted to experience significant range contractions in future climate change scenarios. Geographical factors and environmental factors in southern China have played a critical role in establishing the current genetic diversity and population structure of C. oliveri. This study provides an important reference for forest resource management and conservation for C. oliveri.
Cephalotaxus oliveri / demographic history / ecological niche modeling / genetic differentiation / local adaptation / population transcriptome
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