Combining physical and species‐based approaches improves refugia identification
Bibliographic record
Abstract
Climate‐change refugia – locations likely to facilitate species persistence under climate change – are increasingly important components of conservation planning. Recent approaches for identifying refugia at broad scales include identifying regions that are projected to experience less severe changes (climatic exposure), that contain a diversity of physical and topographic features (environmental diversity), and that either retain or remain close to suitable climatic conditions (climate tracking, including both “species‐neutral” and species‐based approaches). We compared the degree of agreement between these approaches – with respect to their spatial coverage and other characteristics – across much of North America. This analysis found that approaches based on environmental diversity and species‐neutral climatic gradients both favored topographically complex regions, whereas climatic exposure and species‐based approaches identified regions with a range of topographic characteristics. Species‐based approaches targeting specific habitat groups identified unique regions missed by other approaches, emphasizing the importance of asking the question “refugia for what?” when prioritizing refugia. Our results highlight the necessity of including climatic exposure and species‐based information in addition to topographic diversity and climatic gradients in refugia analyses.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".