Monitoring estuarine fish communities: environmental DNA (eDNA) metabarcoding as a complement to beach seining
Bibliographic record
Abstract
Environmental deoxyribonucleic acid (eDNA) metabarcoding offers advantages over physical capture for identifying and quantifying animals in monitoring programs. In this study, the fish community was sampled at three stations (inner, middle, and outer estuary) in three estuaries in August 2020, and four estuaries in June and August 2021 (Prince Edward Island, Canada) using both beach seining and eDNA metabarcoding. Two 12S primer sets, 12S-160 and 12S-248F, with different amplicon lengths, yielded similar results. eDNA metabarcoding consistently detected species captured by 186 co-located beach seines and revealed additional species. It also detected monthly (June–August), interannual (2020–2021), and spatial shifts in the fish community, distinguishing stations separated by as little as 0.4 km. Positive correlations existed between eDNA metabarcoding species reads and beach seining captures. These findings suggest eDNA metabarcoding complements physical capture methods for characterizing nearshore fish communities in Prince Edward Island’s estuaries. While eDNA techniques lack certain population parameter information provided by physical methods, such as size, sex, and age structure, they offer a more comprehensive diversity assessment and presence–abundance insights, especially in inaccessible environments.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".