Wild sunflower goes viral: Citizen science and comparative genomics allow tracking the origin and establishment of invasive sunflower in the Levant
Bibliographic record
Abstract
Globalization and intensified volume of trade and transport around the world are accelerating the rate of biological invasions. It is therefore increasingly important to understand the processes through which invasive species colonize new habitats, often to the detriment of native flora. The initial steps of an invasion are particularly critical, as the introduced species relies on limited genetic diversity to adapt to a new environment. However, our understanding of this critical stage of the invasion is currently limited. We used a citizen science approach and social media to survey the distribution of invasive sunflower in Israel. We then sampled and sequenced a representative collection and compared it with available genomic data sets of North American wild sunflower, landraces and cultivars. We show that invasive wild sunflower is rapidly establishing throughout Israel, probably from a single, recent introduction from Texas, while maintaining high genetic diversity through ongoing gene flow. Since its introduction, invasive sunflower has spread quickly to most regions, and differentiation was detected despite extensive gene flow between clusters. Our findings suggest that rapid spread followed by continuous gene flow between diverging populations can serve as an efficient mechanism for maintaining sufficient genetic diversity at the early stages of invasion, promoting rapid adaptation and establishment in the new territory.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".