Quantifying relative fish abundance with <scp>eDNA</scp>: a promising tool for fisheries management
Bibliographic record
Abstract
Summary Assessment and monitoring of exploited fish populations are challenged by costs, logistics and negative impacts on target populations. These factors therefore limit large‐scale effective management strategies. Evidence is growing that the quantity of eDNA may be related not only to species presence/absence, but also to species abundance. In this study, the concentrations of environmental DNA ( eDNA ) from a highly prized sport fish species, Lake Trout Salvelinus namaycush (Walbaum 1792) , were estimated in water samples from 12 natural lakes and compared to abundance and biomass data obtained from standardized gillnet catches as performed routinely for fisheries management purposes. To reduce environmental variability among lakes, all lakes were sampled in spring, between ice melt and water stratification. The eDNA concentration did not vary significantly with water temperature, dissolved oxygen, pH and turbidity, but was significantly positively correlated with relative fish abundance estimated as catch per unit effort ( CPUE ), whereas the relationship with biomass per unit effort ( BPUE ) was less pronounced. The value of eDNA to inform about local aquatic species distribution was further supported by the similarity between the spatial heterogeneity of eDNA distribution and spatial variation in CPUE measured by the gillnet method. Synthesis and applications . Large‐scale empirical evidence of the relationship between the eDNA concentration and species abundance allows for the assessment of the potential to integrate eDNA within fisheries management plans. As such, the eDNA quantitative method represents a promising population abundance assessment tool that could significantly reduce the costs associated with sampling and increase the power of detection, the spatial coverage and the frequency of sampling, without any negative impacts on 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".