Balancing selection maintains diversity in a cold tolerance gene in broadly distributed live oaks
Bibliographic record
Abstract
Cold poses major physiological challenges to plants, especially long-lived trees. In trees occurring along variable temperature clines, the expected direction and consequences of selection on cold acclimation ability and freezing tolerance are not straightforward. Here we estimated selection in cold acclimation genes at two evolutionary timescales in all seven species of the American live oaks (Quercus subsection Virentes). Two cold response candidate genes were chosen: ICE1, a key gene in the cold acclimation pathway, and HOS1, which modulates cold response by negatively regulating ICE1. Two housekeeping genes, GAPDB and CHR11, were also analyzed. At the shallow evolutionary timescale, we demonstrate that HOS1 experienced recent balancing selection in the two most broadly distributed species, Q. virginiana and Q. oleoides. At a deeper evolutionary scale, a codon-based model of evolution revealed the signature of negative selection in ICE1. In contrast, three positively selected codons have been identified in HOS1, possibly a signature of the diversification of Virentes into warmer climates from a freezing adapted lineage of oaks. Our findings indicate that evolution has favored diversity in cold tolerance modulation through balancing selection in HOS1 while maintaining core cold acclimation ability, as evidenced by purifying selection in ICE1.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".