Propagule pressure, genetic structure, and geographic origins of <i>Chondrilla juncea</i> (Asteraceae): An apomictic invader on three continents
Bibliographic record
Abstract
PREMISE OF THE STUDY: Assessing propagule pressure and geographic origins of invasive species provides insight into the invasion process. Rush skeletonweed (Chondrilla juncea; Asteraceae) is an apomictic, perennial plant that is invasive in Australia, South America (Argentina), and North America (Canada and the United States). This study comprehensively compares propagule pressure and geographic structure of genotypes to improve our understanding of a clonal invasion and enhance management strategies. • METHODS: We analyzed 1056 native range plants from Eurasia and 1156 plants from three invaded continents using amplified fragment length polymorphism (AFLP) techniques. We used measures of diversity (Simpson's D) and evenness (E), analysis of molecular variance, and Mantel tests to compare invasions, and genotype similarity to determine origins of invasive genotypes. • KEY RESULTS: We found 682 unique genotypes in the native range, but only 13 in the invaded regions. Each invaded region contained distinct AFLP genotypes, suggesting independent introduction events, probably with different geographic origins. Relatively low propagule pressure was associated with each introduction around the globe, but levels of among-population variation differed. We found exact AFLP genotype matches between the native and invaded ranges for five of the 13 invasive genotypes. • CONCLUSIONS: Invasion dynamics can vary across invaded ranges within a species. Intensive sampling for molecular analyses can provide insight for understanding intraspecific invasion dynamics, which can hold significance for the management of plant species, especially by finding origins and distributions of invasive genotypes for classical biological control efforts.
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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) | 0.000 | 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 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".