Transboundary conservation hotspots in China and potential impacts of the belt and road initiative
Bibliographic record
Abstract
Abstract Aim Biodiversity hotspots often span international borders, thus conservation efforts must as well. China is one of the most biodiverse countries and the length of its international land borders is the longest in the world; thus, there is a strong need for transboundary conservation. We identify China's transboundary conservation hotspots and analyse the potential effects of the Belt and Road Initiative (BRI) on them to provide recommendations for conservation actions. Location China, Asia. Methods We compiled a species list of terrestrial vertebrates that span China's borders. Using their distribution, we extracted the top 30% of the area with the highest richness value weighted by Red List category and considered these transboundary hotspots for conservation priority. Then we analysed protected area (PA) coverage and connectivity to identify conservation gaps. To measure potential impact of the BRI, we counted the species whose distribution range is traversed by the BRI, and calculated the aggregation index, proportion of natural land and night light index along its routes. Results We identified 1964 terrestrial vertebrate species living in the border region. We identified four transboundary hotspots and found insufficient PA coverage and low connectivity in three of them. The BRI routes intersected all four hotspots and traversed 82.4% (1619/1964) of the transboundary species, half of which (918) are sensitive to the potential risks brought by the BRI. Night light index increased generally along the BRI. However, the proportion of natural land and the aggregation index near the BRI showed different trends in hotspots. Main Conclusions There is an urgent need for conservation action in China's transboundary region. The BRI should put biodiversity conservation at the core of its development strategy. Furthermore, we suggest using the planned BRI as a platform for dialogue and consultation, knowledge and data sharing, and joint planning to promote transboundary conservation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".