Hybridization Slows Rate of Evolution in Crop-Wild Compared to Wild Populations of Weedy Raphanus Across a Moisture Gradient
Bibliographic record
Abstract
Hybrid offspring of crops and their wild relatives commonly possess non-adaptive phenotypes and diminished fitness. Regularly, diminished success in early-generation hybrid populations is interpreted to suggest reduced biosafety risk regarding the unintended escape of novel traits from crop populations. Yet hybrid populations have been known to evolve to recover fitness relative to wild progenitors and can do so more rapidly than wild populations, although rates of evolution (for both hybrid populations and their wild progenitors) are sensitive to environmental context. In this research, we asked whether hybrid populations evolved more rapidly than wild populations in the context of soil moisture. We estimated evolutionary rates for 40 Raphanus populations that varied in their history of hybridization and environmental context (imposed by an experimental moisture cline) in two common gardens. After five generations of growing wild and crop-wild hybrid populations across a soil-moisture gradient, hybrid populations exhibited increased seedling emergence frequencies (~6% more), earlier emergence (~1 day), later flowering (~3 days), and larger body size (15–35%)—traits correlated with fitness—relative to wild populations. Hybrid populations, however, exhibited slower evolutionary rates than wild populations. Moreover, the rate of evolution in hybrid populations was consistent across evolutionary watering environments, but varied across watering environments in wild populations. These consistent evolutionary rates exhibited in hybrid populations suggests the evolution of robust traits that perform equally across soil moisture environments—a survival strategy characterized as “jack of all trades.” Although, diverse integrated weed management practices must be applied to wild and hybrid genotypes to diversify selection on these populations, evaluating the evolutionary rates of weeds in diverse environments will support the development of multi-faceted weed control strategies and effective integrated weed management policies.
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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 | 0.001 |
| 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 | 0.000 | 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".