Monitoring and predicting the presence and abundance of juvenile Atlantic salmon in tributaries according to habitat characteristics using environmental <scp>DNA</scp>
Bibliographic record
Abstract
Abstract Conservation of the Atlantic salmon Salmo salar requires to monitor the spatial distribution and abundance of juveniles at a local scale in tributaries. However, tributaries are rarely accounted for in monitoring programs despite their importance for juvenile life stages. This is mainly because inventories of young salmon populations in tributaries can be technically challenging with traditional methods, as the number of tributaries in a watershed can be important and their access limited compared to the main stem. In this study, we tested the use of environmental DNA (eDNA) to quantify the abundance of juvenile Atlantic salmon in tributaries. We successfully detected eDNA of juvenile Atlantic salmon in 19 tributaries of three main rivers of the Gaspé Peninsula (Québec, Canada) using quantitative real‐time PCR analyses. By comparing the eDNA approach with electrofishing surveys conducted in parallel to water sampling, we found that eDNA concentrations positively correlated with juvenile abundance, total biomass, and body surface area. The use of the allometrically scaled mass (ASM) instead of abundance improved the correlation. Furthermore, we demonstrated that the levels of eDNA molecules detected for juvenile Atlantic salmon were also correlated with water temperature and canopy cover measured in each tributary. Finally, we tested if eDNA concentrations measured in a tributary could be used as a reliable indicator of juvenile abundance or biomass in that tributary. We found that our models slightly better predicted juvenile biomass than juvenile abundance. The use of ASM did not improve model prediction, suggesting that further refinement would be required in the future. Our method will facilitate the implementation of conservation practices appropriate to the ecology of juvenile Atlantic salmon in tributaries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".