Comparing environmental metabarcoding and trawling survey of demersal fish communities in the Gulf of St. Lawrence, Canada
Bibliographic record
Abstract
Abstract Biodiversity assessment is an important part of conservation management that ideally can be accomplished with noninvasive methods without influencing the structure and functioning of ecosystems. Environmental DNA (eDNA) metabarcoding has provided a promising tool to enable fast and comprehensive monitoring of entire ecosystems, but widespread adoption of this technique requires performance evaluations that compare it with conventional surveys. We compared eDNA metabarcoding and trawling data to evaluate their efficiency to characterize demersal fish communities in the Estuary and Gulf of Saint‐Lawrence, Canada. Seawater and bottom trawling samples were collected in parallel at 84 stations. For a subset of 30 of these stations, water was also collected at three different depths (15, 50, and 250 m) across the water column. An eDNA metabarcoding assay based on the 12S mitochondrial gene using the MiFish‐U primers was applied to detect fish eDNA. We detected a total of 88 fish species with both methods combined, with 72 species being detected by eDNA, 64 species detected by trawl, and 47 species (53%) overlapped between both methods. eDNA was more efficient for quantifying species richness, mainly because it detected species known to be less vulnerable to trawling gear. Our results indicated that the relative abundance estimated by eDNA and trawl is significantly correlated for species detected by both methods, while the relationship was also influenced by environmental variables (temperature, depth, salinity, and oxygen). Integrating eDNA metabarcoding to bottom trawling surveys could provide additional information on vertical fish distribution in the water column. Environmental DNA metabarcoding thus appears to be a reliable and complementary approach to trawling surveys for documenting fish biodiversity, including for obtaining relative quantitative estimates in the marine environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".