Conservation priority corridors enhance the effectiveness of protected area networks in China
Bibliographic record
Abstract
The expansion and interconnection of protected areas are central to achieving the Kunming-Montreal Global Biodiversity Framework’s ambitious goals, yet their synergistic relationship remains underexplored. Here, we propose a framework integrating wildlife dispersal-based connectivity to address two key objectives in China: (1) constructing a cost-effective nature conservation network by combining connectivity and biodiversity prioritization, and (2) evaluating climate and anthropogenic risks while addressing habitat representation gaps. The framework aims to designate 30% of land as protected areas and informally allocate additional 30% of land as conservation priority corridors. Results show this strategy connects 57% of existing protected areas, protects 74% of priority zones, and achieves 89% of habitat representation targets. While current protected areas mitigate climate and anthropogenic threats, future expansion faces challenges due to geographic variations in these threats and the necessity for adequate representation. Our approach identifies and prioritizes these challenges, offering a data-driven pathway to achieve Kunming-Montreal targets. The integration of protected areas into conservation priority corridors in China can effectively connect 57% of existing protected areas, safeguard 74% of priority conservation areas, and achieve 89% of habitat representation targets, according to a connectivity and biodiversity conservation framework
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".