Assessing the effectiveness of China's protected areas to conserve current and future amphibian diversity
Bibliographic record
Abstract
Abstract Aim Protected areas are an important tool for conserving species. In this study, we assessed the effectiveness of protected areas to conserve amphibian biodiversity in response to future changes in climate and land use. Location China. Methods Range maps and occurrence records of amphibian species in China were analysed separately using ensemble species distribution modelling across three spatial scales to assess scale dependency. Climate velocity and corresponding residence time in protected areas and species’ ranges were calculated, together with a number of other effectiveness indices. Results Predicted declines in amphibian richness, endemism, phylogenetic diversity, phylogenetic endemism and suitable habitat were lower in protected than in unprotected areas, complementary‐priority sites or richness hotspots. However, less‐disturbed amphibian habitat, calculated from current and future projected land use data, in both protected and unprotected areas were consistently lost over time although this reduction was lower in protected areas. Although residence time of precipitation was longer in protected areas and within species’ ranges in protected areas, resident time of temperature was significantly shorter in both. These results were consistent regardless of data sources and spatial scales. Main conclusions China's current protected areas are predicted to maintain future amphibian distribution and diversity, but are insufficient in preventing the losses of suitable climate and areas of less‐disturbed habitat. The top 10% of future conservation gaps for amphibians were identified in China based on performance of effectiveness indices. The two largest gaps prioritized for future protected areas include the southern parts of Tibet and the Hengduan Mountains.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".