Defensive Mechanisms of Mikania micrantha Likely Enhance Its Invasiveness as One of the World’s Worst Alien Species
Bibliographic record
Abstract
Mikania micrantha Kunth is native to tropical America and has invaded tropical and subtropical Asia and numerous Pacific Islands. It forms dense thickets and reduces native species diversity and populations in its introduced range. This invasive vine also seriously impacts many agricultural crops and is listed as one of the world’s 100 worst invasive alien species. Its life history characteristics, such as the production of large numbers of wind-dispersed seeds, vegetative reproduction, rapid growth, and genetic diversity all contribute to its invasiveness. In this review, we focus on how mechanisms to defend against its natural enemies boost the invasiveness of M. micrantha. It possesses potent defenses against natural enemies such as pathogenic fungi, herbivorous insects, and parasitic nematodes, and exhibits allelopathic potential against plant competitors. These defensive abilities, in concert with its formidable life history characteristics, contribute to the invasiveness of M. micrantha, potentially leading to further naturalization. Several other reviews have summarized the biology and management of the species, but ours is the first review to focus on how the defensive mechanisms of M. micrantha likely enhance its invasiveness. Relatively little is known about the array of defensive capabilities of M. micrantha; therefore, there is considerable scope for further research on its chemical defenses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".