Validation of an environmental DNA protocol to detect a stream‐breeding amphibian, the Streamside Salamander (<i>Ambystoma barbouri</i>)
Bibliographic record
Abstract
Abstract Environmental DNA (eDNA), or DNA that is shed into the environment by an organism, can be used to detect the presence of cryptic species. However, eDNA methodology requires validation of an assay in both laboratory and field environments. Here, we describe the development of a quantitative PCR (qPCR) assay and field protocol for detecting a secretive amphibian, the Streamside Salamander ( Ambystoma barbouri). This fossorial species is rarely encountered because adults are only active for several months during the winter when they breed and deposit eggs underneath rocks within intermittent streams. We designed and validated a qPCR assay for A. barbouri against five ambystomatid congeners and the Southern Two‐lined Salamander ( Eurycea cirrigera ), which occur within the range of A. barbouri in the focal study area. We detected DNA of A. barbouri in the laboratory to 0.0004 ng/µl, while all other congeners were rarely detected below 40 ng/µl. We collected 1‐L water samples from 45 streams from December 2016 to May 2017 and during April 2018 to validate our methods in the field. Our assay was effective at detecting A. barbouri in 24 of 45 streams. We confirmed physical presence of A. barbouri (adults, larvae, and/or eggs) at 21 out of 24 of these positive sites. The detection probability was 0.85 ± 0.05; CI: 0.75, 0.95 within a single sampling event that incorporated 5 water samples from each of 17 repeat‐visit sites. The per water sample sensitivity was 68% (163/238) and specificity was 100% (22/22). The per site visit sensitivity was 85% (46/54) and specificity was 100% (6/6). Lastly, the per site sensitivity was 95% (21/22) and specificity was 100% (3/3). Collectively, this protocol outlines an efficient and cost‐effective method to detect A. barbouri and provides a technique to rapidly identify sites where breeding is occurring and to determine the distribution of this species.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".