Rapid parallel adaptation in distinct invasions of Ambrosia artemisiifolia is driven by large-effect structural variants
Bibliographic record
Abstract
When introduced to multiple distinct ranges, invasive species provide a compelling natural experiment for understanding the repeatability of adaptation. Ambrosia artemisiifolia is an invasive, noxious weed and chief cause of hay fever. Leveraging over 400 whole-genome sequences spanning the native range in North America and two invasions in Europe and Australia, we inferred demographically distinct invasion histories on each continent. Despite substantial differences in genetic source and effective population size changes during introduction, scans of both local climate adaptation and divergence from the native range revealed genomic signatures of parallel adaptation between invasions. Disproportionately represented among these parallel signatures are 37 large haploblocks - indicators of structural variation - that cover almost 20% of the genome and exist as standing genetic variation in the native range. Many of these haploblocks are associated with traits important for adaptation to local climate, like size and the timing of flowering, and have rapidly reformed native range clines in invasive ranges. Others show extreme frequency divergence between ranges, consistent with a response to divergent selection on different continents. Our results demonstrate the key role of large-effect standing variants in rapid adaptation during range expansion, a pattern that is robust to diverse invasion histories.
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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 | 0.000 |
| 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".