Identifying climate change refugia for South American biodiversity
Bibliographic record
Abstract
Abstract Refugia‐based conservation offers long‐term effectiveness and minimize uncertainty on strategies for climate change adaptation. We used distribution modelling to identify climate change refugia for 617 terrestrial mammals and to quantify the role of protected areas (PAs) in providing refugia across South America. To do so, we compared species potential distribution across different scenarios of climate change, highlighting those regions likely to retain suitable climatic conditions by year 2090, and explored the proportion of refugia inside PAs. Moist tropical forests in high‐elevation areas with complex topography concentrated the highest local diversity of species refugia, although regionally important refugia centers occurred elsewhere. Andean–Amazon forests contained climate change refugia for more than half of the continental species’ pool and for up to 87 species locally (17 × 17 km 2 grid cell). The highlands of the southern Atlantic Forest also included megadiverse refugia for up to 76 species per cell. Almost half of the species that may find refugia in the Atlantic Forest will do so in a single region—the Serra do Mar and Serra do Espinhaço. Most of the refugia we identified, however, were not in PAs, which may contain <6% of the total area of climate change refugia, leaving 129–237 species with no refugia inside the territorial limits of PAs of any kind. Our results reveal a dismal scenario for the level of refugia protection in some of the most biodiverse regions of the world. Nonetheless, because refugia tend to be in high‐elevation, topographically complex, and remote areas, with lower anthropogenic pressure, formally protecting them may require a comparatively modest investment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".