The role of northern shrimp as a key forage species in the Canadian sub-Arctic
Bibliographic record
Abstract
Key forage species are essential to the structure and functioning of marine ecosystems by connecting energy flow from primary producers to predators. In sub-Arctic ecosystems, northern shrimp Pandalus borealis plays an important ecological role as prey for higher trophic level consumers, including commercially harvested species. Northern shrimp is the subject of an important fishery in northeastern Canada, the third most valuable in Newfoundland and Labrador in 2024. Despite its ecological and economic importance, the impact of predation on northern shrimp population dynamics is largely unknown, particularly in the northernmost Canadian fishing areas. We addressed this issue by quantifying the role played by 6 fish predator taxa in consuming northern shrimp. Stomach contents from 3556 fish samples were analyzed to characterize predator feeding habits, quantify the contribution of northern shrimp to fish diets, and estimate total consumption of shrimp. Results indicated spatiotemporal variation in predation pressure on northern shrimp. Total predator biomass was identified as a key driver of shrimp biomass dynamics, suggesting that increased predator densities may create predation hotspots that exert localized pressure on shrimp stocks. Stomach content analysis showed regional variation in fish diet composition, with northern shrimp consistently serving as an important prey species, particularly for Greenland halibut Reinhardtius hippoglossoides and Atlantic cod Gadus morhua . Consumption models revealed notable variability in shrimp consumption across years, with a marked increase observed from 2020 onward. In northernmost regions, continued monitoring is essential to validate observed consumption trends, establish key ecological roles of forage species, and capture emerging ecological shifts.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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".