Challenging growth–survival trade-off: a key for Acer negundo invasion in European floodplains?
Bibliographic record
Abstract
We compared the performances of juvenile Acer negundo with those of native species to assess how this species has invaded intermediate habitats along European riparian successional gradients. In the middle Rhône floodplain (France), we measured survival and growth of transplants of the invasive and of three native tree species from contrasted successional status within forests and in experimental gaps and at three positions along a riparian gradient: (i) a highly disturbed Salix – Populus stand, (ii) a moderately disturbed stand dominated by the invasive Acer , and (iii) a mature Fraxinus community. Acer’s growth in the gaps was as high as that of the two native early-successional species, Salix and Populus, and higher than that of the native late-successional Fraxinus. In contrast, Acer survived as well in the shadiest understory conditions of the Fraxinus community as did Fraxinus and better than the two early-successional species. Inconsistent with the resource trade-off of succession theory, Acer showed both a high survival in the shade and a high growth in full light. This particular suite of traits shared with other invasive and native Acer species could be an example of adaptive plasticity that certainly represents an advantage to give it a competitive advantage over native species.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".