Genomic Signatures of Environmental Adaptation in Castanopsis hainanensis (Fagaceae)

As an endemic <i>Castanopsis</i> species on Hainan Island, <i>Castanopsis hainanensis</i> 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 <i>C. hainanensis</i> and offers actionable insights for informed conservation strategies to mitigate climate-driven biodiversity loss.

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Publication Details

Journal
Plants
Published
2025-04-05
DOI
https://doi.org/10.3390/plants14071128
Citations
1
Primary Topic
Silkworms and Sericulture Research
Type
article
Field-Weighted Citation Impact
1.67

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article

Genomic Signatures of Environmental Adaptation in Castanopsis hainanensis (Fagaceae)

Sha Li, Ye Sun, Xing Chen, Yang Wu
1 citations
Plants
Silkworms and Sericulture Research
1.67
article

Genomic Signatures of Environmental Adaptation in Castanopsis hainanensis (Fagaceae)

Sha Li, Ye Sun, Xing Chen, Yang Wu
article en
1 citations

Abstract

As an endemic <i>Castanopsis</i> species on Hainan Island, <i>Castanopsis hainanensis</i> 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 <i>C. hainanensis</i> and offers actionable insights for informed conservation strategies to mitigate climate-driven biodiversity loss.

PlantsVol. 14(7)
South China Agricultural University (CN)
China Agricultural University, South China Agricultural University
Openalex Percentile: Top 20%
Silkworms and Sericulture Research
1.67
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