Overfishing and Climate Change Elevate Extinction Risk of Endemic Sharks and Rays in the Southwest Indian Ocean Hotspot
Bibliographic record
Abstract
Abstract The southwest Indian Ocean (SWIO) is a hotspot of endemic and evolutionarily distinct sharks and rays. We summarise the extinction risk of the sharks and rays endemic to coastal, shelf, and slope waters of the SWIO (Namibia to Kenya, including SWIO islands). Thirteen of 70 species (19%) are threatened: one is Critically Endangered, five are Endangered, and seven are Vulnerable. A further seven (10%) are Near Threatened, 33 (47.1%) are Least Concern, and 17 (24.2%) are Data Deficient. While the primary threat is overfishing, there are the first signs that climate change is contributing to elevated extinction risk through habitat reduction and inshore distributional shifts. By backcasting their status, few species were threatened in 1980, but this changed soon after the emergence of targeted shark and ray fisheries. South Africa has the highest national conservation responsibility, followed by Mozambique and Madagascar. Yet, while fisheries management and enforcement have improved in South Africa over recent decades, drastic improvements are urgently needed elsewhere. To avoid extinction and ensure robust populations and future food security, there is an urgent need for the strict protection of Critically Endangered and Endangered species and sustainable management of all species, underpinned by species-level data collection and bycatch reduction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".