Overfishing drives over one-third of all sharks and rays toward a global extinction crisis
Bibliographic record
Abstract
The scale and drivers of marine biodiversity loss are being revealed by the International Union for Conservation of Nature (IUCN) Red List assessment process. We present the first global reassessment of 1,199 species in Class Chondrichthyes-sharks, rays, and chimeras. The first global assessment (in 2014) concluded that one-quarter (24%) of species were threatened. Now, 391 (32.6%) species are threatened with extinction. When this percentage of threat is applied to Data Deficient species, more than one-third (37.5%) of chondrichthyans are estimated to be threatened, with much of this change resulting from new information. Three species are Critically Endangered (Possibly Extinct), representing possibly the first global marine fish extinctions due to overfishing. Consequently, the chondrichthyan extinction rate is potentially 25 extinctions per million species years, comparable to that of terrestrial vertebrates. Overfishing is the universal threat affecting all 391 threatened species and is the sole threat for 67.3% of species and interacts with three other threats for the remaining third: loss and degradation of habitat (31.2% of threatened species), climate change (10.2%), and pollution (6.9%). Species are disproportionately threatened in tropical and subtropical coastal waters. Science-based limits on fishing, effective marine protected areas, and approaches that reduce or eliminate fishing mortality are urgently needed to minimize mortality of threatened species and ensure sustainable catch and trade of others. Immediate action is essential to prevent further extinctions and protect the potential for food security and ecosystem functions provided by this iconic lineage of predators.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".