Development of <scp>eDNA</scp> Protocols for Detection of Endangered White Sturgeon (<i>Acipenser transmontanus</i>) in the Wild
Bibliographic record
Abstract
ABSTRACT Understanding the distribution and habitat use of endangered species is essential for conservation efforts. Environmental DNA (eDNA) analysis has become a more common approach to defining species habitat occupancy through identification of residual DNA in water samples and has potential to detect populations that are in low abundance or use habitats over a large geographical range. Here, we optimized an eDNA protocol to detect the presence of the endangered white sturgeon ( Acipenser transmontanus ). We implemented lab‐based experiments to understand the sensitivity and persistence of white sturgeon eDNA and then applied these methods to habitats with known white sturgeon abundances categorized as high, low, or not present. Using quantitative PCR (qPCR) and a modified StrAci1N‐flap primer set, white sturgeon eDNA was detected in water collected from tanks holding white sturgeon down to a dilution of 10,000× (estimated eDNA concentration of 0.00035 μg/L—0.00176 μg/L). Following the removal of white sturgeon from the tanks, the eDNA signal decreased with time but could be detected for up to 7 days. In the field, all sites with high abundances of white sturgeon returned positive eDNA detections. We did not detect white sturgeon eDNA at sites with low abundance or in areas where they were not expected to be present. Results from this work further advance our interpretation of eDNA from wild populations and provide a noninvasive method to advance recovery efforts by identifying species presence in areas of suspected use or to guide additional inventory efforts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.002 |
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".