Reduced fitness under drought stress in F1 hybrids of <i>Antirrhinum majus</i> varieties with divergent flower colors
Bibliographic record
Abstract
PREMISE: What maintains trait divergence in the face of gene flow? Two varieties of wild snapdragon (Antirrhinum majus) characterized by divergent flower color hybridize in their native range. Selection on flower color genes is indicated by sharp clines, but the selective agents have not been demonstrated. Although previous work has focused on pollinators, pigmentation genes can also contribute to abiotic stress tolerance. We hypothesized that pigmentation in A. majus mediates stress tolerance, which could contribute to hybrid zone maintenance through parental niche divergence or hybrid maladaptation. Specifically, we tested whether morphotype mediates drought tolerance in an experiment comparing magenta-flowered var. pseudomajus, yellow-flowered var. striatum, and their pink-flowered hybrid cross. METHODS: We experimentally compared drought tolerance of each morphotype from allopatric crosses within and between varieties using three greenhouse treatments. Control plants were watered as needed, while drought-treated plants were watered half as often, either from the transplant stage ("early" drought), or from flowering onset ("late" drought). RESULTS: Parental morphotypes responded identically to drought in fitness and most phenotypic traits. However, hybrids had lower survival (14%) under late drought stress than parental morphotypes (70%). All hybrids that flowered in the late drought treatment died, compared to ~20% of flowering parental morphotypes. CONCLUSIONS: Hybrid maladaptation to abiotic stress could potentially contribute to flower color divergence in the face of gene flow in A. majus. Further research should test the relevance of our results to field conditions and explicitly probe the role of flower color genes in drought tolerance.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".