Tracking Long‐Term Trends in Sockeye Salmon <i>(Oncorhynchus nerka)</i> Population Dynamics Using Sterol and Stanol Biomarkers in Lake Sediments
Bibliographic record
Abstract
Abstract We analyzed biomarkers, including sterols, stanols, and δ 15 N, in sediment cores from lakes with well‐documented sockeye salmon return histories. Our goal was to improve estimates of past changes in salmon escapement, that is, the population that return to their freshwater nursery lakes, inferred using sediment biogeochemical markers. Cholesterol, the predominant sterol in adult sockeye salmon muscle tissue, displayed a strong positive relationship with escapement ( R 2 = 0.8, p = 0.001, and F 1,8 = 28.3). Sediment concentrations of the plant‐derived sitosterol and algal‐derived fucosterol, absent in salmon muscle tissue also related positively with salmon escapement, suggesting that salmon‐derived nutrients from decomposing fish promote the production of these lipids by primary producers in the lakes. We developed a novel salmon sterol index (SSI a ) from values in surface sediments of the nine Alaskan lakes [(cholesterol + coprostanone + epicoprostanol + desmosterol)/(cholesterol + coprostanone + epicoprostanol + desmosterol + fucosterol + sitosterol + stigmastanol)] that was strongly related with salmon return density (pseudo R 2 = 0.86 and RMSE = 0.071). This index also tracked historical sockeye escapement patterns and δ 15 N values in 210 Pb‐dated lake sediment cores from Frazer, Karluk, Red, and Kinaskan lakes that span more than a century of salmon population history, suggesting that the index has potential as a proxy for tracking historical salmon populations, particularly when used in combination with independent biomarkers of salmon‐derived nutrient inputs. SSI a and the other salmon sterol indices we developed show promise for improving and extending long‐term sockeye salmon population estimates using lake sediment records, which will help inform salmon conservation and management efforts.
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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 | 0.000 | 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 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".