Admixture With Cultivated Sunflower Likely Facilitated Establishment and Spread of Wild Sunflower (<i>Helianthus annuus</i>) in Argentina
Bibliographic record
Abstract
A better understanding of the genetic and ecological factors underlying successful invasions is critical to mitigate the negative impacts of invasive species. Here, we study the invasion history of Helianthus annuus populations from Argentina, with particular emphasis on the role of post-introduction admixture with cultivated sunflower (also H. annuus) and climate adaptation driven by large haploblocks. We conducted genotyping-by-sequencing of samples of wild populations as well as Argentinian cultivars and compared them with wild (including related annual Helianthus species) and cultivated samples from the native range. We also characterised samples for 11 known haploblocks associated with environmental variation in native populations to test whether haploblocks contributed to invasion success. Population genomics analyses supported two independent geographic sources for Argentinian populations, the central United States and Texas, but no significant contribution of related annual Helianthus species. We found pervasive admixture with cultivated sunflower, likely as result of post-introduction hybridization. Genomic scans between invasive populations and their native sources identified multiple genomic regions of divergence, possibly indicative of selection, in the invaded range. These regions significantly overlapped between the two native-invasive comparisons and showed disproportionally high crop ancestry, suggesting that crop alleles contributed to invasion success. We did not find evidence of climate adaptation mediated by haploblocks, yet outliers of genome scans were enriched in haploblock regions and, for at least two haploblocks, the cultivar haplotype was favoured in Argentina. Our results show that admixture with cultivated sunflower played a major role in the establishment and spread of H. annuus populations in Argentina.
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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 | 0.000 | 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".