Genomic insights into alpine plant adaptation
Bibliographic record
Abstract
Alpine plants persist in some of the harshest terrestrial environments, where low temperatures, high ultraviolet radiation, and short growing seasons impose strong selective pressures. Recent advances in genome sequencing and comparative genomics are unraveling the multifaceted mechanisms that enable their adaptation and diversification under these conditions. In this review, we synthesize current progress on how genetic variation at different levels, including single nucleotide polymorphisms (SNPs), structural variants, whole-genome duplication, gene family evolution, and transposable elements, contribute to high-elevation adaptations in alpine plants. SNP-based studies have provided critical insights into adaptive differentiation along environmental gradients as well as molecular convergence underlying high-elevation adaptation, while analyses of structural variations and transposable elements reveal their potential roles in shaping phenotypic diversity and environmental responsiveness. Despite these advances, major challenges remain in linking genomic variation to functional adaptation, reflecting limitations in sampling, comparative frameworks, and functional validation. This review emphasizes the promise of integrative multi-omics, pangenome reconstruction, and functional assays to bridge these gaps, and highlights how genomic insights can guide the conservation of alpine biodiversity under accelerating climate change. • Genomic studies are transforming our understanding of diverse mechanisms by which alpine plants adapt to high-elevation environments. • Structural variants, whole-genome duplication, and gene family evolution underpin lineage-specific and convergent adaptations. • Transposable element dynamics contribute to genomic plasticity and stress responsiveness in alpine environments. • Integrative multi-omics and pangenomes offer new opportunities to link genomic variation with ecological function and conservation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".