Integrating climate‐change exposure and refugia into landscape planning: A practical guide
Bibliographic record
Abstract
Abstract Climate change is reshaping landscapes in ways that challenge conventional approaches to conservation and resource planning. The concept of climate‐change refugia—areas with the potential to buffer species and ecosystems from the effects of climate change—offers a valuable lens for identifying strategic opportunities for long‐term stewardship. Building on this foundation, we present a flexible, climate‐informed approach to landscape planning that integrates climate‐change exposure and refugia information into a five‐step process: (1) define core ecological, cultural, and land resource values and identify those most at risk; (2) assess landscape capacity as a function of climate‐change exposure and conservation capacity (i.e., landscape condition); (3) develop place‐based strategies and identify relevant spatial data products; (4) incorporate macrorefugia, microrefugia, and corridors to align land‐use designations with strategies; and (5) implement, monitor, and adaptively refine refugia‐based planning over time. Recognizing variation in planning needs and contexts, our guidance supports the practical use of spatial refugia metrics to inform land‐use, conservation, and resource management decisions.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".