Genetic Variation Related to High Elevation Adaptation Revealed by Common Garden Experiments in Pinus yunnanensis
Bibliographic record
Abstract
Local adaptation, adaptation to specialized niches and environmental clines have been extensively reported for forest trees. Investigation of the adaptive genetic variation is crucial for forest resource management and breeding, especially in the context of global climate change. Here, we set up long-term common garden experiments in high and low elevation sites for Pinus yunnanensis. Significant differences of growth and survival were observed among populations and between common garden sites. By comparing the survival populations between sites, we detected genetic variation related to high elevation adaptation. We captured 103,608 high quality SNPs based on RNA sequencing, and used them to assess the genetic diversity and population structure. We identified 321 outlier SNPs from 131 genes showing significant divergence in allelic frequency between survival populations of two sites. Functional categories associated with adaptation to high elevation were found to be related to flavonoid biosynthesis, response to UV, DNA repair, response to reactive oxygen species, and membrane lipid metabolic process. Further investigation of the outlier genes showed overrepresentation of the flavonoid biosynthesis pathway, suggesting this pathway may play a key role in the adaptation to high elevation in P. yunnanensis. The outlier genes identified in this study, and their variants, provide a basic reference for advanced investigations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".