Hybridization speeds adaptive evolution in an eight-year field experiment
Bibliographic record
Abstract
Hybridization is a common phenomenon, yet its evolutionary outcomes remain debated. Here, we ask whether hybridization can speed adaptive evolution using resynthesized hybrids between two species of Texas sunflowers (Helianthus annuus and H. debilis) that form a natural hybrid in the wild (H. annuus ssp. texanus). We established separate control and hybrid populations and allowed them to evolve naturally in a field evolutionary experiment. In a final common-garden, we measured fitness and a suite of key traits for these lineages. We show that hybrid fitness evolved in just seven generations, with fitness of the hybrid lines exceeding that of the controls by 14% and 51% by the end of the experiment, though only the latter represents a significant increase. More traits evolved significantly in hybrids relative to controls, and hybrid evolution was faster for most traits. Some traits in both hybrid and control lineages evolved in an adaptive manner consistent with the direction of phenotypic selection. These findings show a causal pathway from hybridization to rapid adaptation and suggest an explanation for the frequently noted association between hybridization and adaptive radiation, range expansion, and invasion.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".