Climate change may reduce the spread of non‐native species
Bibliographic record
Abstract
Abstract Invasive species are considered a major threat to ecosystem functioning and native biodiversity. Their negative impacts on ecosystems and the provisioning of ecosystem services have been widely documented. South Africa faces one of the most significant challenges from invasive species globally, and the South African government spent an estimated US $100 million to mitigate impacts of non‐native species between 1995 and 2000 alone. Here, we modeled the current climatic niche of 162 non‐native trees and shrubs within South Africa and explored potential shifts in their distribution with projected climate change. Our results indicate that over half of these species will experience a decrease in their suitable climate over the next decades, although not uniformly so and ranges are predicted to expand into some regions. We also compared recent vs. historical introductions and showed similar patterns, indicating that possible violation of equilibrium assumptions in our distribution models likely does not strongly influence our findings. We suggest that climate change may therefore provide a window of opportunity for more effective invasive species control within South Africa, but that large range shifts are likely for many non‐natives in the future, and new invasive threats might emerge.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".