Estimating occupancy and abundance of stream amphibians using environmental DNA from filtered water samples
Bibliographic record
Abstract
Environmental DNA (eDNA) methods for detecting aquatic species are advancing rapidly, but with little evaluation of field protocols or precision of resulting estimates. We compared sampling results from traditional field methods with eDNA methods for two amphibians in 13 streams in central Idaho, USA. We also evaluated three water collection protocols and the influence of sampling location, time of day, and distance from animals on eDNA concentration in the water. We found no difference in detection or amount of eDNA among water collection protocols. eDNA methods had slightly higher detection rates than traditional field methods, particularly when species occurred at low densities. eDNA concentration was positively related to field-measured density, biomass, and proportion of transects occupied. Precision of eDNA-based abundance estimates increased with the amount of eDNA in the water and the number of replicate subsamples collected. eDNA concentration did not vary significantly with sample location in the stream, time of day, or distance downstream from animals. Our results further advance the implementation of eDNA methods for monitoring aquatic vertebrates in stream habitats.
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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 | 0.000 |
| 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".