Measuring protected-area outcomes with leech iDNA: large-scale quantification of vertebrate biodiversity in Ailaoshan nature reserve
Bibliographic record
Abstract
1 Abstract Protected areas are central to meeting biodiversity conservation goals, but measuring their effectiveness is challenging. We address this challenge by using DNA from leech-ingested bloodmeals to estimate vertebrate occupancies across the 677 km 2 Ailaoshan reserve in Yunnan, China. 163 park rangers collected 30,468 leeches from 172 patrol areas. We identified 86 vertebrate species, including amphibians, mammals, birds, and squamates. Multi-species occupancy modelling showed that species richness increased with elevation and distance to reserve edge, including the distributions of most of the large mammals (e.g. sambar, black bear, serow, tufted deer). The exceptions were the three domestic mammal species (cows, sheep, goats) and muntjak deer, which were more common at lower elevations. eDNA-estimated vertebrate occupancies are Granular, Repeatable, Auditable, Direct, Efficient , and Simple-to-understand measures that can be used to assess conservation effectiveness and thus to improve the contributions that protected areas make to achieving global biodiversity goals.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".