Tracking long-term trends in Sockeye salmon ( Oncorhynchus nerka ) population dynamics using sterol and stanol biomarkers in lake sediments
Bibliographic record
Abstract
We examined a combination of sediment biomarkers, including sterols, stanols, and δ 15 N, in lakes with well documented Sockeye salmon return histories to optimize methods to infer past changes in salmon escapement (i.e. the population that returns to their freshwater nursery lakes) based on dated sediment core records. Several sterols in surface sediment correlated strongly with salmon escapement across nine Alaskan lakes, particularly cholesterol, the predominant sterol in adult Sockeye salmon muscle tissue (R 2 = 0.8, p = 0.001, F 1,8 = 28.3). Sediment concentrations of the plant-derived sitosterol and algal-derived fucosterol, absent in salmon muscle tissue, also correlated positively with salmon escapement, suggesting that salmon-derived nutrients from decomposing salmon indirectly promote the production of these lipids by primary producers within the aquatic ecosystem. We developed a novel salmon sterol index (SSIa) [(cholesterol + coprostanone + epicoprostanol + desmosterol) / (cholesterol + coprostanone + epicoprostanol + desmosterol + fucosterol + sitosterol + stigmastanol)] that correlated most strongly with salmon return density in surface sediments across nine Alaskan lakes (Pseudo R 2 = 0.86, RMSE =0.071). This index also tracked Sockeye escapement patterns and δ 15 N values in dated sediments spanning over a century of salmon population history, suggesting its potential as a proxy for tracking historical salmon populations, particularly when used alongside independent proxies of salmon-derived nutrient inputs. In summary, the SSIa and the other SSIs we developed show promise to enhance and extend long-term Sockeye salmon population studies from lake sediment records that 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 | 0.001 | 0.001 |
| 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".