Prioritizing conservation corridors to enhance connectivity and mitigate multidimensional vulnerabilities in protected area networks
Bibliographic record
Abstract
Expanding and connecting protected areas (PAs) is crucial to meet the ambitious goals of the Kunming-Montreal Global Biodiversity Framework (GBF), yet the interplay between these targets remains poorly understood. Here we introduce a novel framework for developing conservation priority corridors (CPCs) that balances connectivity, cost-effectiveness, and biodiversity value. Our framework integrates critical connectivity corridors with conservation priority zones, identifying priority areas for conservation action across three scenarios: conservative, moderate, and ambitious. We demonstrate that the moderate CPC scenario offers a pragmatic pathway to achieving the GBF's targets in China, increasing PA coverage to 34%, effectively connecting half of existing PAs, and safeguarding nearly 40% of conservation priority zones. While CPCs face heightened climate-related threats, they exhibit lower vulnerability to human activities and vegetation shifts, highlighting the critical role of connectivity in enhancing resilience to climate change. Our CPC framework identifies the most vulnerable regions and pressing conservation challenges, providing valuable guidance for targeted conservation efforts. This integrative approach offers a practical and impactful pathway to achieving the ambitious goals of the Kunming-Montreal GBF.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".