Distribution and protection of climatic refugia in North America
Bibliographic record
Abstract
As evidenced by past climatic refugia, locations projected to harbor remnants of present-day climates may serve as critical refugia for current biodiversity in the face of modern climate change. We mapped potential climatic refugia in the future across North America, defined as locations with increasingly rare climatic conditions. We identified these locations by tracking projected changes in the size and distribution of climate analogs over time. We used biologically derived thresholds to define analogs and tested the impacts of dispersal limitation with 4 distances to limit analog searches. We identified at most 12% of North America as potential climatic refugia. Refugia extent varied depending on the analog threshold, dispersal distance, and climate projection. However, in all cases refugia were concentrated at high elevations and in topographically complex regions. Refugia identified using different climate projections were largely nested, suggesting that identified refugia were relatively robust to climate-projection selection. Existing conservation areas cover approximately 10% of North America and yet protected up to 25% of identified refugia, indicating that protected areas disproportionately include refugia. Refugia located at lower latitudes (≤40°N) and slightly lower elevations (approximately 2500 m) were more likely to be unprotected. Based on our results, a 23% expansion of the protected-area network would be sufficient to protect the refugia present under all 3 climate projections we explored. We believe these refugia are high conservation priorities due to their potential to harbor rare species in the future. However, these locations are simultaneously highly vulnerable to climate change over the long term. These refugia contracted substantially between the 2050s and the 2080s, which supports the idea that the pace of climate change will strongly determine the availability and effectiveness of refugia for protecting today's biodiversity.
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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 | 0.000 |
| Research integrity | 0.000 | 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".