eDNA and Acoustic Tag Monitoring Reveal Congruent Overwintering Distributions of Striped Bass in a Hydrologically Complex Estuarine Environment
Bibliographic record
Abstract
After collapsing in the late 1990s, the southern Gulf of St. Lawrence population of striped bass (Morone saxatilis) is recovering. Here, we evaluate the use of under-ice eDNA sampling to monitor the population and confirm overwintering locations. From 2018 to 2020, water samples were collected from transects spanning 35 km of the Miramichi River system, accounting for the effects of sampling site, month, sampling depth and tidal influence on eDNA concentration. We examined the distribution of eDNA in a complex tidal river system with a time series consisting of 12 h of continuous sampling throughout a tidal cycle, in conjunction with the use of artificial DNA tracers and acoustic Doppler current profiler flow measurements. The eDNA distribution correctly identified overwintering grounds based on acoustic tag data, including a perceptible upstream shift in 2020. Overall, there was no significant effect of year, sampling month (February or March), sampling depth or tidal phase on eDNA concentrations. The tidal time series revealed only weak patterns of eDNA recirculation. Monitoring eDNA concentration and distribution allows for a relative comparison of population size and location between years, and has the potential to be expanded to other river systems more easily than traditional acoustic fish tags and surveys.
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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 | 0.001 |
| 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) | 0.000 | 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 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".