Genetics and evolution of weedy <i>Helianthus annuus</i> populations: adaptation of an agricultural weed
Bibliographic record
Abstract
Agricultural weeds are a major cost to economies throughout the world, and have evolved from numerous plant species in many different plant families. Despite their ubiquity, we do not yet know how easily or often weeds evolve from their wild ancestors or the kinds of genes underlying their evolution. Here we report on the evolution of weedy populations of the common sunflower Helianthus annuus. We analysed 106 microsatellites in 48 individuals from each of six wild and four weed populations of the species. The statistical tests lnRV and lnRH were used to test for significant reductions in genetic variability at each locus in weedy populations compared to nearby wild populations. Between 1% and 6% of genes were significant outliers with reduced variation in weedy populations, implying that a small but not insignificant fraction of the genome may be under selection and involved in adaptation of weedy sunflowers. However, there did not appear to be a substantial reduction in variation across the genome, suggesting that effective population sizes have remained very large during the recent evolution of these weedy populations. Additional analyses showed that weedy populations are more closely related to nearby wild populations than to each other, implying that weediness likely evolved multiple times within the species, although a single origin followed by gene flow with local populations cannot be ruled out. Together, our results point to the relative ease with which weedy forms of this species can evolve and persist despite the potentially high levels of geneflow with nearby wild populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".