Fish community shifts along a strong fluvial environmental gradient revealed by eDNA metabarcoding
Bibliographic record
Abstract
Abstract Large rivers and their estuaries are structurally complex and comprise a diversity of habitats supporting a rich biodiversity. As a result, identifying and monitoring fish communities using traditional methods in such systems may often be logistically challenging. Using the mitochondrial DNA 12S MiFish primers, we performed an eDNA metabarcoding analysis to assess the effect of spatial and environmental factors on the variation of the fish community structure along most of the St. Lawrence River/Estuary/Gulf (Québec Canada), a transect spanning 1300 km across a diversity of habitats from a fluviatile non‐tidal section to a marine environment. A total of 129 species were identified including freshwater and marine species. For the freshwater sectors, eDNA identified 80 species compared with the 85 species previously reported based on conventional sampling. eDNA also revealed similar species diversity and communities in the fluviatile section of the St. Lawrence River. Furthermore, our study improved current knowledge about the brackish and marine sections by describing community transition between freshwater and marine fish communities in association with a drastic shift in environmental conditions observed between the end of the fluvial estuary and the beginning of the middle (brackish) estuary. Altogether, this study exemplifies how eDNA metabarcoding is a powerful tool to document fish community shifts in large temperate lotic ecosystems.
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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 | 0.000 |
| 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".