Relationship between brook charr (<i>Salvelinus fontinalis</i>) <scp>eDNA</scp> concentration and angling data in structured wildlife areas
Bibliographic record
Abstract
Abstract Accurate management of exploited fish populations is essential to ensure their long‐term sustainability. The use of eDNA as a tool for providing information on population relative abundance offers much potential although few examples in a fishery context have been documented. In this study, we collected 600 water samples from 30 lakes in Québec (Canada) to document the relationship between brook charr ( Salvelinus fontinalis ) angling data and their lake's eDNA concentration. Model selection with angling data and environmental parameters was used to find the best predictive model for eDNA concentration. We found a strong correlation between the average fish density from current and previous years (fish harvested/ha, adj. R 2 = 0.76) with the mean eDNA concentration among lakes, supporting the growing trends in the literature. We observed very similar levels of correlation either when eDNA and angling data were from the same year or different years. We also found a pronounced inter‐year difference in lakes' eDNA quantity measured in 2019 and 2020. We hypothesize that the main drivers for this difference were inter‐seasonal variation including water temperature and associated variation in fish behavior. These results support the usefulness of eDNA as a quantitative tool for exploited fish populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".