Forecasting Buffelgrass (Cenchrus ciliaris) Distributions In Southern Arizona Under Multiple Climate Change Scenarios
Bibliographic record
Abstract
Buffelgrass (Cenchrus ciliaris) is an invasive grass that can alter fire regimes, reduce local biodiversity, and convert complex arid ecosystems into buffelgrass dominated grasslands. As buffelgrass populations continue to grow, it will be important to be able to predict which areas are most susceptible to future buffelgrass invasion. This study attempts to provide some insight into this problem by creating a model to predict changes in the extent of potential buffelgrass habitat under different climate change scenarios between 2020 and 2100. Maximum entropy modelling was conducted using known occurrences of buffelgrass in the Santa Catalina mountains of Southern Arizona in combination with 19 bioclimatic variables from WorldClim to create a baseline model, which was then applied to future climatic conditions under the Canadian Earth Systems Model 5 (CanESM5) for three different climate change scenarios. The maximum entropy method produced an accurate model with an area under curve (AUC) value of 0.9913 and in validation trails it was able to accurately predict the presence of buffelgrass with 91.37% accuracy. When applied to future climatic conditions, the model showed a 280% increase in potential buffelgrass habitat under light and moderate climate change scenarios, and a 501% increase under a more severe scenario. Considering this potential for buffelgrass to spread, it may be essential for land managers to aggressively combat buffelgrass introductions to prevent it from being able to spread further and continue to damage ecosystems, as well as emphasize the importance of minimizing the impacts of climate change.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".