Assessing the trophic ecology of Southern Ocean Myctophidae: the added value of DNA metabarcoding
Bibliographic record
Abstract
Lanternfishes (Myctophidae) are key components of mesopelagic fish communities globally. In the Southern Ocean, incomplete information on myctophid diets limits our understanding of their energetics, interactions, and wider ecosystem impact. Traditional microscopic methods of diet analysis have relatively coarse prey resolution and possible taxonomic and observer biases. Deoxyribonucleic acid (DNA) metabarcode sequencing promises higher taxonomic and temporal resolution, but uncertainty remains in comparing this with microscopy-based analyses. Here, we applied 18S DNA metabarcode sequencing to stomach contents from 20 Electrona antarctica individuals that had previously been examined via microscopic analysis. Across all fish analysed, crustacean and gastropod taxa dominated the prey identified via both methods, with broad agreement between methods on the relative abundance of different prey items. DNA metabarcode sequencing recovered greater taxonomic diversity and resolution, particularly for soft-bodied prey items and small crustaceans. DNA sequencing results also more clearly differentiated diet between individuals collected from different environments. Overall, our findings illustrate how DNA-based methods are complementary to, and consistent with, traditional methods and can provide additional, high-resolution data on a range of trophic interactions.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".