Monitoring Atlantic Salmon (<i>Salmo salar</i>) Smolt Migration in a Large River System Using Environmental <scp>DNA</scp>
Bibliographic record
Abstract
ABSTRACT Effective monitoring strategies are key for aquatic species conservation, but traditional methods often require significant resources, especially in large aquatic systems. The emergence of quantitative environmental DNA (eDNA) is a promising alternative. Yet, few studies have evaluated the possibility of quantifying Atlantic salmon abundance with eDNA when individuals are at low abundance and occur in large river systems. In this study, we tested the efficacy of eDNA to monitor and quantify daily variation in smolt counts during their downstream migration in a large river system with low abundance of smolts. During the 2021 and 2022 downstream migrations, trap nets were used to conduct a daily census of smolts in the Romaine and Puyjalon rivers (Québec, Canada) while eDNA samples were collected daily over a transect perpendicular to the riverbank. Using quantitative real‐time qPCR, we showed that discharge‐corrected eDNA concentrations were positively correlated with daily smolt counts for both years. In addition, we found that controlling for temperature and precipitation improved model transferability between years, showing the importance of considering environmental correlates when using eDNA for abundance quantification. Finally, smolt counts were correlated with eDNA concentrations on the same day, but not with eDNA concentrations 1 or 2 days prior, highlighting the capacity of the model to track daily fluctuations in smolt abundance. Our results underscore the potential of using eDNA to monitor Atlantic salmon in large river systems with low smolt abundance when the river hydrology and environmental conditions are documented.
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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 | 0.000 | 0.000 |
| 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".