Hybrid evolution repeats itself across environmental contexts in Texas sunflowers ( <i>Helianthus</i> )
Bibliographic record
Abstract
To what extent is evolution repeatable? Little is known about whether the evolution of hybrids is more (or less) repeatable than that of nonhybrids. We used field experimental evolution in annual sunflowers (Helianthus) in Texas to ask the extent to which hybrid evolution is repeatable across environments compared to nonhybrid controls. We created hybrids between Helianthus annuus (L.) and H. debilis (Nutt.) and grew plots of both hybrids and nonhybrid controls through eight generations at three sites in Texas. We collected seeds from each generation and grew each generation × treatment × home site combination at two final common gardens. We estimated the strength and direction of evolution in terms of fitness and 24 traits, tested for repeated versus nonrepeated evolution, and assessed overall phenotypic evolution across lineages and in relation to a locally adapted phenotype. Hybrids consistently evolved higher fitness over time, while controls did not, although trait evolution varied in strength across home sites. Repeated evolution was more evident in hybrids versus nonhybrid controls, and hybrid evolution was often in the direction of the locally adapted phenotype. Our findings have implications for both the nature of repeatability in evolution and the contribution of hybridization to evolution across environmental contexts.
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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 | 0.000 |
| 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".