Environmental DNA detection of endangered and invasive species in Kejimkujik National Park and Historic Site
Bibliographic record
Abstract
The use of environmental DNA (eDNA) allows the early detection of aquatic species at low densities (e.g., elusive and invasive species), which otherwise could be challenging to monitor using conventional techniques. Here, we assess the ability of eDNA sampling to detect the presence or absence of one species at risk (Blanding's turtle) and two invasive species (chain pickerel and smallmouth bass) in Kejimkujik National Park and National Historic Site, Nova Scotia, where the aquatic system is highly acidic and rich in organic compounds. Five replicates of 1 L water samples were taken per sampling site. Water filtration and eDNA extractions were performed on-site, while qPCR reactions were performed in the laboratory using species-specific assays. Samples were treated with an inhibition removal kit and analyzed pre- and post-inhibition removal. Despite the low pH and PCR inhibitors in water samples, our results showed positive eDNA detections in almost all expected positive sites (except in one site for Blanding's turtle). Detections of the target species were also observed at sites where their presence was previously unknown. Our study supports the advantage of eDNA to monitor species at low densities, revealing new distributions or recently invaded areas. We also demonstrate how eDNA can directly instruct management strategies in Kejimkujik.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".