Global Assessment of Extinction Risk to Populations of Sockeye Salmon Oncorhynchus nerka
Bibliographic record
Abstract
BACKGROUND: Concern about the decline of wild salmon has attracted the attention of the International Union for the Conservation of Nature (IUCN). The IUCN applies quantitative criteria to assess risk of extinction and publishes its results on the Red List of Threatened Species. However, the focus is on the species level and thus may fail to show the risk to populations. The IUCN has adapted their criteria to apply to populations but there exist few examples of this type of assessment. We assessed the status of sockeye salmon Oncorhynchus nerka as a model for application of the IUCN population-level assessments and to provide the first global assessment of the status of an anadromous Pacific salmon. METHODS/PRINCIPAL FINDINGS: We found from demographic data that the sockeye salmon species is not presently at risk of extinction. We identified 98 independent populations with varying levels of risk within the species' range. Of these, 5 (5%) are already extinct. We analyzed the risk for 62 out of 93 extant populations (67%) and found that 17 of these (27%) are at risk of extinction. The greatest number and concentration of extinct and threatened populations is in the southern part of the North American range, primarily due to overfishing, freshwater habitat loss, dams, hatcheries, and changing ocean conditions. CONCLUSIONS/SIGNIFICANCE: Although sockeye salmon are not at risk at the species-level, about one-third of the populations that we analyzed are at risk or already extinct. Without an understanding of risk to biodiversity at the level of populations, the biodiversity loss in salmon would be greatly underrepresented on the Red List. We urge government, conservation organizations, scientists and the public to recognize this limitation of the Red List. We also urge recognition that about one-third of sockeye salmon global population diversity is at risk of extinction or already extinct.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".