Atlantic puffin diet reflects haddock and redfish abundance in the Gulf of Maine
Bibliographic record
Abstract
Ecosystem-based fisheries management, which considers the interactions between fisheries, target species, and the physical and biological components of ecosystems, is necessary to ensure that directed fisheries avoid adverse impacts to ecosystems over the long term. The successful implementation of ecosystem-based fisheries management requires an understanding of predator-prey relationships and ways to operationalize such relationships to inform fisheries management. Here, we investigated if the diet of a generalist predator, Atlantic puffinFratercula arctica, can be used as an indicator of the abundance of 2 commercially exploited prey species (haddockMelanogrammus aeglefinusand Acadian redfishSebastes fasciatus) in the Gulf of Maine. Because haddock and redfish eaten by puffins are juveniles (age 0), there is potential to use their proportions and lengths in puffin diet to better understand the processes influencing haddock and redfish recruitment. By using principal component analysis to develop measures of diet across multiple puffin colonies, we show both spatial variation and large-scale patterns in the proportions and lengths of haddock and redfish in puffin diet. Spawning stock biomass was a strong predictor of haddock proportion in puffin diet and a moderate predictor of redfish proportion; however, proportions in puffin diet did not predict age-1 recruitment, suggesting that variation in recruitment is caused by processes that occur after the puffin breeding season and which affect the survival of older juveniles. Haddock length on one colony was a moderate predictor of age-1 recruitment. We conclude that puffin diet can be used as an indicator of haddock and redfish abundance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".