Nutrient variability increases dominance of two invasive plants in the field
Bibliographic record
Abstract
A fluctuating resource supply makes some of the most problematic invasive plants more successful against native plants. However, the evidence so far comes from simplified pot experiments in the greenhouse or garden, and it remains an open question whether these mechanisms are also relevant in natural conditions. Here, we present an experiment that tested the effects of nutrient fluctuations on plant invasion in the field. In eight sites in South-West Germany invaded by Asian knotweed (Reynoutria x bohemica) or Canadian goldenrod (Solidago canadensis), we manipulated the temporal patterns of nutrient availability by applying liquid fertilizer at weekly intervals over a 10-week period. There were three treatments: (1) constant nutrient supply, with equal amounts at every application, (2) variable nutrients supply, with double amount of nutrient as in the constant treatment but added at every second application, totaling the same total amount for the entire experiment, and (3) control with water only. We found that the invaders did not benefit from the additional nutrients when they were supplied in a constant manner but became more dominant after several months of variable nutrient supply. To our knowledge, this is the first field evidence that resource fluctuations can promote invasive plants in natural communities and shows that fluctuating resources can alter invasion success even within a single growing season. The key questions are now how general these effects are across other invasive plants and possibly also strong native ruderals, and what the longer-term effects of the fluctuating resources are on community composition and diversity.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".