Spatiotemporal variability in diet composition of Greenland halibut (<scp><i>Reinhardtius hippoglossoides</i></scp>) from the eastern Canadian Arctic
Bibliographic record
Abstract
Greenland halibut (Reinhardtius hippoglossoides) sustain one of the most lucrative fisheries in the eastern Canadian Arctic and Labrador Sea. This species also plays an important role in food web connectivity and benthic-pelagic coupling. Despite the relatively rich knowledge of this species, R. hippoglossoides ecology in these specific areas remains poorly understood. The main aim of this study was to characterize the diet of this deepwater fish in the Labrador Sea and Davis and Hudson Straits and characterize the predator-prey relationship with northern shrimp (Pandalus borealis), another commercially important species in the region. Stomach contents analyses were conducted on 1199 fish captured from 2018 to 2020. Small specimens (<20 cm) fed on invertebrates, whereas larger individuals (>60 cm) fed primarily on fish, indicative of size-related changes in diet composition. The relative abundance of Pandalus shrimp species in the environment was reflected in the diet. Location appeared to be the most influential variable on feeding patterns. Distinct oceanographic conditions among areas, resulting in differences in prey availability, could explain these results. Arctic cod (Boreogadus saida) and redfish (Sebastes sp.) were selected in locations where fish prey were the most abundant. These results shed light on the opportunistic nature of R. hippoglossoides and its preference for fish at large size. With the rapidly changing oceanographic conditions of Arctic waters, a distributional change in the biomass of shrimp is expected. Results suggest that an increase in abundance of predatory groundfish species in the system (e.g., Sebastes sp.) could lead to acute predation on shrimp and competition with R. hippoglossoides. By revealing key trophic links within the demersal ecosystem, this work provides valuable information on the development of ecosystem approaches to fisheries management for the region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".