Red‐list status and extinction risk of the world's whales, dolphins, and porpoises
Bibliographic record
Abstract
To understand the scope and scale of the loss of biodiversity, tools are required that can be applied in a standardized manner to all species globally, spanning realms from land to the open ocean. We used data from the International Union for the Conservation of Nature Red List to provide a synthesis of the conservation status and extinction risk of cetaceans. One in 4 cetacean species (26% of 92 species) was threatened with extinction (i.e., critically endangered, endangered, or vulnerable) and 11% were near threatened. Ten percent of cetacean species were data deficient, and we predicted that 2-3 of these species may also be threatened. The proportion of threatened cetaceans has increased: 15% in 1991, 19% in 2008, and 26% in 2021. The assessed conservation status of 20% of species has worsened from 2008 to 2021, and only 3 moved into categories of lesser threat. Cetacean species with small geographic ranges were more likely to be listed as threatened than those with large ranges, and those that occur in freshwater (100% of species) and coastal (60% of species) habitats were under the greatest threat. Analysis of odontocete species distributions revealed a global hotspot of threatened small cetaceans in Southeast Asia, in an area encompassing the Coral Triangle and extending through nearshore waters of the Bay of Bengal, northern Australia, and Papua New Guinea and into the coastal waters of China. Improved management of fisheries to limit overfishing and reduce bycatch is urgently needed to avoid extinctions or further declines, especially in coastal areas of Asia, Africa, and South America.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".