Climate change may reduce the spread of non‐native species
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.167 | 0.005 |
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 it