Plastic responses of invasive <i>Bidens frondosa</i> to water and nitrogen addition
Bibliographic record
Abstract
Invasive species are hypothesized to be more plastic than co‐occurring native congeners, and variation in plasticity among invasive populations is predicted to facilitate invasion of new habitats. To explore the invasive ability of Bidens frondosa , we compared the plastic responses to water and nitrogen addition of the invasive B. frondosa in China with the co‐occurring native congener B. tripartita , as well as among B. frondosa populations. The invasive plant performed better and showed higher phenotypic plasticity to water and nitrogen addition than the native. In addition, variations in performance and phenotypic plasticity were observed among the invasive populations. The biomass of the HN (Henan province) population increased more than that of other populations in response to nitrogen addition. The specific leaf area (SLA) of the GX (Guangxi province) population increased, while the SLA of the HN population decreased, and the HB (Hebei province) and EZ (Hubei province) populations showed no change in response to nitrogen addition. The observed higher phenotypic plasticity of B. frondosa relative to B. tripartita , and the observed variation in plasticity among B. frondosa populations may explain the invasiveness of this species. Predicted future increases in precipitation and atmospheric N deposition may further increase the invasiveness of B. frondosa .
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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 | 0.001 |
| 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".