Development and validation of an eDNA protocol for monitoring endemic Asian spiny frogs in the Himalayan region of Pakistan
Bibliographic record
Abstract
Wildlife monitoring programs are instrumental for the assessment of species, habitat status, and for the management of factors affecting them. This is particularly important for species found in freshwater ecosystems, such as amphibians, as they have higher estimated extinction rates than terrestrial species. We developed and validated two species-specific environmental DNA (eDNA) protocols and applied them in the field to detect the Hazara Torrent Frog (Allopaa hazarensis) and Murree Hills Frog (Nanorana vicina). Additionally, we compared eDNA surveys with visual encounter surveys and estimated site occupancy. eDNA surveys resulted in higher occurrence probabilities for both A. hazarensis and N. vicina than for visual encounter surveys. Detection probability using eDNA was greater for both species, particularly for A. hazarensis. The top-ranked detection model for visual encounter surveys included effects of both year and temperature on both species, and the top-ranked occupancy model included effects of elevation and year. The top-ranked detection model for eDNA data was the null model, and the top-ranked occupancy model included effects of elevation, year, and wetland type. To our knowledge, this is the first time an eDNA survey has been used to monitor amphibian species in the Himalayan region.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".