Climate-change impacts and fisheries management challenges in the North Atlantic Ocean
Bibliographic record
Abstract
Climate-induced changes in the world’s oceans will have implications for fisheries productivity and management. Using a model ensemble from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP), we analyzed future trajectories of climate-change impacts on marine animal biomass and associated environmental drivers across the North Atlantic Ocean and within the Northwest Atlantic Fisheries Organization (NAFO) convention area and evaluated potential consequences for fisheries productivity and management. Our ensemble results showed that the magnitude of projected biomass changes increased over time and from a low (RCP2.6) to high (RCP8.5) emissions scenario. Within individual NAFO divisions, however, projected biomass changes differed in the magnitude and sometimes direction of change between near (the 2030s) and far future (the 2090s) and contrasting emissions scenarios. By the 2090s, most NAFO divisions with historically (1990-1999) high fisheries landings were projected to experience biomass decreases of 5-40%, while Arctic and subarctic divisions with lower historical landings were projected to experience biomass increases between 20 and 70% under RCP8.5. Future trajectories of sea surface temperature and primary production corroborated that the far-future, high-emissions scenario poses the greatest risk to marine ecosystems and the greatest challenges to fisheries management. Our study summarizes future trends of marine animal biomass and underlying uncertainties related to model projections under contrasting climate-change scenarios. Understanding such climate-change impacts on marine ecosystems is imperative for ensuring that marine fisheries remain productive and sustainable in a changing ocean.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".