Harp seals have a greater impact than fisheries on the stalled cod recovery in Newfoundland and Labrador
Bibliographic record
Abstract
Newfoundland and Labrador has both a large pinniped population and an important fishing industry. Harp seals are the most abundant pinniped in the region, with an estimated population of 4.4 million in 2024. Harp seals consume prey species that are also targeted by fisheries. Many shared target species, including Atlantic cod and capelin, experienced a stock collapse in the early 1990s and have not recovered to pre-collapse levels despite decades of reduced fishing effort. Fishers have raised concerns that harp seal predation may be hindering fish stock recovery. Here, we assess the relative impacts of harp seals and fisheries on shared target species by synthesizing harp seal diet estimates from various methods, harp seal consumption rates, fisheries catch rates, and prey mortality rates. The most common harp seal prey include Arctic cod, Atlantic and Greenland cod Gadus spp., capelin, Atlantic herring, and shrimp, although prey importance varies through space and time. From 2018 to 2020, 24 times more biomass of exploited fish species was consumed by harp seals than harvested by fisheries (tonnes km -2 yr -1 ), with average harp seal predation mortality rates for these species 17 times higher than fishing mortality. In 1985-1987, before the cod collapse, harp seals consumed 1.7 times more exploited fish species than were harvested by fisheries, and predation mortality rates were 1.3 times greater than fishing mortality rates. Our findings indicate that harp seal impacts on prey species are considerably higher than those of fisheries, and that harp seals play a larger role than fisheries in the stalled recovery of groundfish stocks.
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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 | 0.001 | 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) | 0.002 | 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".