Li Sha, Chen Xing, Wu Yang, Sun Ye
Guangdong Key Laboratory for Innovative Development and Utilization of Forest Plant Germplasm, College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China.
Plants (Basel). 2025 Apr 5;14(7):1128. doi: 10.3390/plants14071128.
As an endemic species on Hainan Island, Merr. is uniquely adapted to tropical climatic conditions and occupies a relatively narrow habitat range. Given its long generation times, limited dispersal capacity, and ecological and economic importance, understanding the genomic processes shaping this dominant tree species is critical for conservation. Its adaptation to specialized habitats and distinct geographical distribution provide valuable insights into biodiversity challenges in island ecosystems. This study employs genome-wide single-nucleotide polymorphism (SNP) markers to investigate genetic structure, population dynamics, and adaptive variation. Analyses revealed weak genetic divergence among populations, suggesting high gene flow. Demographic reconstruction indicated a historical population bottleneck, consistent with MaxEnt modeling projections of future range contraction under climate change. Selective sweep and genotype-environment association (GEA) analyses identified SNPs strongly correlated with environmental variables, particularly moisture and temperature. Using these SNPs, we quantified the risk of non-adaptedness (RONA) across climate scenarios, pinpointing regions at heightened vulnerability. Gene Ontology (GO) enrichment highlighted the key genes involved in plant growth and stress adaptation. By integrating genomic and environmental data, this study establishes a framework for deciphering adaptive mechanisms of and offers actionable insights for informed conservation strategies to mitigate climate-driven biodiversity loss.
作为海南岛的特有物种,Merr. 独特地适应了热带气候条件,栖息地范围相对狭窄。鉴于其较长的世代时间、有限的扩散能力以及生态和经济重要性,了解塑造这种优势树种的基因组过程对于保护至关重要。它对特殊栖息地的适应和独特的地理分布为岛屿生态系统中的生物多样性挑战提供了宝贵的见解。本研究采用全基因组单核苷酸多态性(SNP)标记来研究遗传结构、种群动态和适应性变异。分析揭示了种群间微弱的遗传分化,表明基因流较高。种群统计学重建表明存在历史种群瓶颈,这与MaxEnt模型对气候变化下未来分布范围收缩的预测一致。选择性清除和基因型-环境关联(GEA)分析确定了与环境变量,特别是湿度和温度密切相关的SNP。利用这些SNP,我们量化了不同气候情景下的非适应性风险(RONA),确定了脆弱性较高的区域。基因本体论(GO)富集突出了参与植物生长和胁迫适应的关键基因。通过整合基因组和环境数据,本研究建立了一个框架来解读[物种名称未给出]的适应机制,并为明智的保护策略提供可操作的见解,以减轻气候驱动的生物多样性丧失。