Understanding invasion history: genetic structure and diversity of two globally invasive plants and implications for their management
Bibliographic record
Abstract
Abstract Aim Resolving the origin of invasive plant species is important for understanding the introduction histories of successful invaders and aiding strategies aimed at their management. This study aimed to infer the number and origin(s) of introduction for the globally invasive species, Macfadyena unguis‐cati and Jatropha gossypiifolia using molecular data. Location Native range: Neotropics; Invaded range: North America, Africa, Europe, Asia, Pacific Islands and Australia. Methods We used chloroplast microsatellites (cpSSRs) to elucidate the origin(s) of introduced populations and calculated the genetic diversity in native and introduced regions. Results Strong genetic structure was found within the native range of M. unguis‐cati, but no genetic structuring was evident in the native range of J. gossypiifolia. Overall, 27 haplotypes were found in the native range of M. unguis‐cati. Only four haplotypes were found in the introduced range, with more than 96% of introduced specimens matching a haplotype from Paraguay. In contrast, 15 haplotypes were found in the introduced range of J. gossypiifolia, with all invasive populations, except New Caledonia, comprising multiple haplotypes. Main conclusions These data show that two invasive plant species from the same native range have had vastly different introduction histories in their non‐native ranges. Invasive populations of M. unguis‐cati probably came from a single or few independent introductions, whereas most invasive J. gossypiifolia populations arose from multiple introductions or alternatively from a representative sample of genetic diversity from a panmictic native range. As introduced M. unguis‐cati populations are dominated by a single haplotype, locally adapted natural enemies should make the best control agents. However, invasive populations of J. gossypiifolia are genetically diverse and the selection of bio‐control agents will be considerably more complex.
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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 | 0.000 | 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".