Fish as predators and prey: <scp>DNA</scp>‐based assessment of their role in food webs
Bibliographic record
Abstract
Fish are both consumers and prey, and as such part of a dynamic trophic network. Measuring how they are trophically linked, both directly and indirectly, to other species is vital to comprehend the mechanisms driving alterations in fish communities in space and time. Moreover, this knowledge also helps to understand how fish communities respond to environmental change and delivers important information for implementing management of fish stocks. DNA-based methods have significantly widened our ability to assess trophic interactions in both marine and freshwater systems and they possess a range of advantages over other approaches in diet analysis. In this review we provide an overview of different DNA-based methods that have been used to assess trophic interactions of fish as consumers and prey. We consider the practicalities and limitations, and emphasize critical aspects when analysing molecular derived trophic data. We exemplify how molecular techniques have been employed to unravel food web interactions involving fish as consumers and prey. In addition to the exciting opportunities DNA-based approaches offer, we identify current challenges and future prospects for assessing fish food webs where DNA-based approaches will play an important role.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".