Life history variation across four decades in a diverse population complex of Atlantic salmon in a large subarctic river
Bibliographic record
Abstract
We used over 154 000 scale samples collected from salmon fisheries in the large River Teno system over a 40-year period to quantify life history diversity and long-term trends. We identified 120 different life history strategies, including combinations of smolt (2–8) and sea ages (1–5) and previous spawning events. Most strategies were rare; 60% of individuals matured after 1 year at sea following 3–5 years in fresh water. Age at maturity changed with an increase in two-sea-winter salmon and previous spawners and a decline in three-sea-winter fish. Smolt age distribution showed a decreasing proportion of age-3 smolts, while that of age-5 smolts increased. Fishing gear and fishing season times selected for fish differing in life history strategies. Temporal variation in life histories reflected changes in both fisheries and the changing environment. There was an inverse relationship between years spent in fresh water and sea age. Biocomplexity was manifested by the multiple year classes (6–11) present in annual runs, which increased with years, reflecting an increase both in previous spawners and sampling effort. The high number of cohorts spawning simultaneously each year indicates strong generational overlap, which has been suggested to maintain genetic diversity and thereby resilience via the portfolio effect.
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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 | 0.000 |
| 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".