Multiple modes of convergent adaptation in the spread of glyphosate-resistant <i>Amaranthus tuberculatus</i>
Bibliographic record
Abstract
The selection pressure exerted by herbicides has led to the repeated evolution of herbicide resistance in weeds. The evolution of herbicide resistance on contemporary timescales in turn provides an outstanding opportunity to investigate key questions about the genetics of adaptation, in particular the relative importance of adaptation from new mutations, standing genetic variation, or geographic spread of adaptive alleles through gene flow. Glyphosate-resistant Amaranthus tuberculatus poses one of the most significant threats to crop yields in the Midwestern United States, with both agricultural populations and herbicide resistance only recently emerging in Canada. To understand the evolutionary mechanisms driving the spread of resistance, we sequenced and assembled the A. tuberculatus genome and investigated the origins and population genomics of 163 resequenced glyphosate-resistant and susceptible individuals from Canada and the United States. In Canada, we discovered multiple modes of convergent evolution: in one locality, resistance appears to have evolved through introductions of preadapted US genotypes, while in another, there is evidence for the independent evolution of resistance on genomic backgrounds that are historically nonagricultural. Moreover, resistance on these local, nonagricultural backgrounds appears to have occurred predominantly through the partial sweep of a single haplotype. In contrast, resistant haplotypes arising from the Midwestern United States show multiple amplification haplotypes segregating both between and within populations. Therefore, while the remarkable species-wide diversity of A. tuberculatus has facilitated geographic parallel adaptation of glyphosate resistance, more recently established agricultural populations are limited to adaptation in a more mutation-limited framework.
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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 | 0.001 | 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".