five

Genetic divergence and local adaptation of Liriodendron driven by heterogeneous environments

收藏
DataCite Commons2025-04-01 更新2025-04-10 收录
下载链接:
https://datadryad.org/dataset/doi:10.5061/dryad.hqbzkh1d7
下载链接
链接失效反馈
官方服务:
资源简介:
Ecological adaptive differentiation alters both the species diversity and intraspecific genetic diversity in forests, thus affecting the stability of forest ecosystems. Therefore, knowledge of the genetic underpinnings of the ecological adaptive differentiation of forest species is critical for effective species conservation. In this study, single-nucleotide polymorphisms (SNPs) from population transcriptomes were used to investigate the spatial distribution of genetic variation in Liriodendron to assess whether environmental variables can explain genetic divergence. We examined the contributions of environmental variables to population divergence and explored the genetic underpinnings of local adaptation using a landscape genomic approach. Niche models and statistical analyses showed significant niche divergence between L. chinense and L. tulipifera, suggesting that ecological adaptation may play a crucial role in driving interspecific divergence. We detected a new fine-scale genetic structure in L. chinense, and divergence of the six groups occurred during the late Pliocene to early Pleistocene. Redundancy analysis (RDA) revealed significant associations between genetic variation and multiple environmental variables. Environmental association analyses identified 67 environmental association loci (EALs; nonsynonymous SNPs) that underwent interspecific or intraspecific differentiation, 28 of which were associated with adaptive genes. These 28 candidate adaptive loci provide substantial evidence for local adaptation in Liriodendron. Our findings reveal ecological adaptive divergence pattern between Liriodendron species and provide novel insight into the role of heterogeneous environments in shaping genetic structure and driving local adaptation among populations, informing future L. chinense conservation efforts.
提供机构:
Dryad
创建时间:
2021-11-10
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作