Large Fisheries Declines Linked to Compound and Extreme Climate Events
Bibliographic record
Abstract
Abstract Climate extremes are increasingly disrupting marine ecosystems and fisheries. However, evidence on extreme ocean temperature effects on fish stocks is mixed, and the combined impacts of heat and productivity extremes remain unclear. Using three decades of global data encompassing 6,659 time-series for 1,246 species across 254 regions, we conducted a risk-based analysis to quantify how local extreme high temperatures and low ocean productivity that these species were exposed to, alone and together, affect local fisheries catches. These events, particularly when compounded, sharply increase the likelihood of local extreme low catch events, especially in tropical and subtropical regions. Species critical to food security and conservation are disproportionately affected, with widespread risks in socio-economically vulnerable countries. Without adaptation, extreme events’ risks are projected to strongly intensify by the mid-21st century. Strengthening monitoring and climate-responsive fisheries management is urgently needed to build resilience.Main Text
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".