Relationships between Coastal Ocean Conditions and Survival Rates of Northeast Pacific Salmon at Multiple Lags
Bibliographic record
Abstract
Abstract We tested the hypothesis that survival rates from spawners to recruits in Pacific salmon Oncorhynchus spp. are primarily related to coastal ocean conditions during migration to the sea and soon after. We correlated measures of survival rate in units of loge(recruits/spawner) for 110 stocks of pink salmon O. gorbuscha, chum salmon O. keta, and sockeye salmon O. nerka with regional‐scale indices of coastal sea surface temperature, sea surface salinity, and upwelling as well as with a large‐scale index of ocean climate. We examined correlations by month and at multiple lags spanning the periods of spawning, freshwater residence, and early ocean residence of salmon. Survival rates of all three salmon species were related to ocean temperatures just prior to, during, and after out‐migration, which are indicative of the early marine conditions experienced by juvenile salmon. This is consistent with the hypothesis that the early marine period is critical to the survival of juvenile salmon. However, survival rates of sockeye salmon were most strongly correlated with coastal sea surface temperature during freshwater residency (i.e., the winter and spring prior to out‐migration). Survival rates of pink salmon were also related to sea surface salinity conditions prior to out‐migration. There was no evidence for any relationship between the survival rates of salmon and coastal upwelling conditions. As in previous studies, we found that correlations between the survival rates of pink or sockeye salmon in Alaska and sea surface temperature have opposite signs from correlations for stocks in British Columbia and Washington at most lags and at both regional and large (basinwide) spatial scales. In general, however, the measures of coastal ocean conditions that we examined explain a relatively small proportion of the environmentally induced variability in salmon survival rates.
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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 | 0.002 |
| 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) | 0.001 | 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".