Increase in Harp Seal Ecosystem Role After the Cod Collapse in Newfoundland & Labrador
Bibliographic record
Abstract
ABSTRACT Pinniped populations have been increasing worldwide, posing challenges for fisheries management, including damage to fishing gear and competition for resources. In the Northwest Atlantic, harp seal numbers have increased from 1.8 million in 1970 to 6.5 million in 1990, stabilising at 4.4 million in 2024—one of the largest pinniped populations in the world. The large number of harp seals is associated with a high rate of prey consumption, raising questions about their impact on exploited and non‐exploited species. In Newfoundland and Labrador, ecosystems were disrupted with collapses of cod and capelin in the 1990s, and these populations have not yet recovered. This study examines the harp seal ecological role and influence on ecosystem structure and function. Using Ecopath with Ecosim ecosystem models, we simulated various harp seal biomass scenarios for three key periods: pre‐collapse (1985–1987), invertebrate dominance (2013–2015), and partial groundfish recovery (2018–2020). These scenarios explored harp seal depletion and recovery, impacts on cod stocks, and ecosystem effects. Simulations revealed that the ecosystem is driven by both top‐down forces from harp seals and bottom‐up forces from capelin, a key forage species. While moderate reductions in harp seal abundance had limited effects on cod, increasing capelin biomass had positive effects on both harp seals and cod. This study highlights the importance of integrating predator effects into ecosystem‐based fisheries management to anticipate change and increase resilience in dynamic marine systems.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".