<scp>DNA</scp> Metabarcoding Improves the Taxonomic Resolution of Visually Determined Diet Composition of Beaked Redfish (<i>Sebastes</i> sp.)
Bibliographic record
Abstract
ABSTRACT Beaked redfishes ( Sebastes fasciatus and Sebastes mentella ) of the northwest Atlantic have recently reached record abundance levels in the estuary and northern Gulf of St. Lawrence, dominated by Sebastes mentella . Knowledge of their diet composition is essential to understand the trophic role that these groundfish play in the ecosystem. The objective of the present study was to compare the performance of visual examination and DNA metabarcoding of stomach contents of the same individual redfish caught in the estuary and northern Gulf of St. Lawrence. Using a universal metazoan mitochondrial cytochrome c oxidase subunit I (COI) marker, we identified a total of 24 taxonomic groups, composed of 22 species and two genera in the content of 185 stomachs with DNA metabarcoding. We compared these results to the 25 prey types, eight identified at the genus and nine at the species level, obtained with visual stomach content analysis (SCA). While both techniques revealed a similar diet composition, our results showed that the SCA and DNA metabarcoding perform differently for particular prey categories, both in terms of detectability and taxonomic resolution, as well as in the estimated relative importance of weight and occurrence in the diet. The use of DNA metabarcoding along with SCA validates and improves the taxonomic resolution of visually determined prey, which supports the concept that both techniques provide useful complementary information on the diet of redfish and likely other fish species.
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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 | 0.000 | 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 | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".