Environmental variation and its effect on the success of crop-wild hybridization in the Raphanus species complex
Bibliographic record
Abstract
Hybridization has been suggested as mechanism that can contribute to adaptive evolution and the success of crop-wild hybrid populations; but this response appears to depend upon environmental context. I explore how environmental variation affects crop trait expression, the strength and direction of selection on crop traits in radish weed populations, and the influence environmental variation has on crop-trait introgression across agricultural landscapes. Using the Raphanus crop-wild complex as a model system to study the environmental sensitivity of crop gene flow into weed populations, I first planted advanced-generation wild and crop-wild hybrid radish plants (that had previously evolved for three generations under relatively dry, relatively wet, or ambient control soil moisture or water-evolved conditions) into sheltered common gardens that were watered with low, ambient, or high soil moisture. From this work, hybridization and watering history did not enhance the success of advanced-generation hybrid plants relative to wild progenitors in Ontario, Canada. Next, I explored how phenotypic plasticity in response to environmental variation may distort a commonly used metric to measure the rate of evolution, the haldane. To determine the extent that plasticity affected estimates of evolutionary rate, I compared haldane estimates of advanced-generation water-evolved plants grown in a common garden that did not involve manipulation of ambient watering conditions. Estimates of the magnitude and direction of contemporary evolution differed significantly due to annual environmental variation, particularly for wild populations. Thus, I propose changes to the use of these equations and changes to the equation itself to help avoid generating false estimates of evolutionary rates. Finally, a meta-analysis of radish phenology and fecundity data collected from the last twelve years across four locations revealed that geography can affect the strength and direction of selection on crop- derived traits in weedy radish populations. This large, integrated study offers environmental risk assessment a new perspective on the role of environmental change on the success of crop-wild hybridization and its ability to generate weedy species. In summary, I provide evidence that environmental variation should be considered before making predictions about a crop trait’s evolutionary trajectory and persistence in a weedy plant population.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".