Proximity and size of protected areas in Asian borderlands enable transboundary conservation
Bibliographic record
Abstract
Asia has over 80% of the Earth’s border hotspots for threatened transboundary wildlife, yet only limited research has been done on the distribution of protected areas across international borders in the continent. To address this gap, we conducted a spatial analysis of protected areas across 42 Asian countries. Our study aimed to understand the distribution, proximity, and land-use changes within border protected areas. Two cases were examined, evaluating the spatial relationships at different buffer distances from international borders. Our findings revealed that Asian countries have larger protected areas in borderlands, particularly up to 50 km from borders, as compared to regions further away from the border. Importantly, the median distance between protected areas across international borders is nearly three times shorter than those within the same country. However, the rate of change in natural habitats within protected areas between 2001 and 2019 showed no correlation with their distance from the border. The proximity of protected areas across Asian borders offers opportunities for enhancing connectivity. A larger extent of multi-use protected areas (IUCN1-6+) near borders compared to strict protected areas (IUCN1-4) can facilitate the engagement of communities, which are crucial in transboundary conservation initiatives. Our results can help Asian countries as they work toward their commitments as part of the Kunming–Montreal Global Biodiversity Framework to protect at least 30% of the Earth’s surface area by 2030.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".