The effects of climate warming and urbanised areas on the future distribution of <i>Cortaderia selloana</i>, pampas grass, in France
Bibliographic record
Abstract
Summary The spread of many invasive plants could be facilitated by their presence in urban areas that may act as dispersal centres and by climate warming. Cortaderia selloana, pampas grass, is native to South America and raises considerable concern worldwide as an introduction. We used Maxent niche modelling, based on occurrence records and on a set of simulated occurrence points with high probability of presence in urbanised areas in France, where the species was introduced and is still planted. We calibrated the model with current climate data coupled with several habitat variables and used it to predict range shifts of C. selloana under four climate change scenarios (RCP) for 2060. The results were consistent with the known ecology of the species and showed that the most important variables that explain the current distribution in the introduced area were mean annual minimum temperatures, sandy habitats, disturbed habitats and urbanised areas. While the species already occupies large areas along the western and Mediterranean coasts, the models predicted an expansion northward and inland to the east under future climates. The area of suitable habitats could increase by up to 69% under the RCP 8.5 climate scenario in 2060 and by 116% with the extra occurrences in urban/suburban areas. This latter scenario suggests that areas like public and private gardens or urban parks, where the species is currently cultivated, could contribute to increase the invasion risk under climate warming. The results provide predictions of potential environments for the species, which can be helpful for anticipating its spread.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".