Intra- and inter-population variation in sensitivity of migratory sockeye salmon smolts to phenological mismatch
Bibliographic record
Abstract
Certain consumer traits may influence sensitivity to phenological mismatches between consumers and their prey, and understanding the variation in these traits across or within populations could be helpful in predicting if and how a consumer population will respond to climate change. Here, we quantify intra- and inter-population variation in traits of sockeye salmon (Oncorhynchus nerka) smolts that may influence sensitivity to starvation associated with phenological mismatch. We asked 2 questions: (1) What is the magnitude of intra- and inter-population variation in physical and energetic condition at different stages of emigration? (2) How would this trait influence survival during periods of starvation? We collected sockeye salmon smolts from 3 populations before and 8 populations after riverine migration within the Skeena River watershed, BC, and measured condition-specific traits such as size and energetic condition. We discovered among-population variation was lower after migration: before migration traits differed between populations, but after-migration traits were more similar across populations. We estimated starvation resistance, the number of days until predicted death, using a previously developed model. Mean starvation resistance varied between 18 and 33 d across populations and varied within each population to as low as 6 d. These results reveal substantial within- and across-population sensitivity to starvation which may be associated with phenological mismatch. Thus, factors other than phenology (e.g. freshwater ecosystem dynamics that influence smolt condition) have the potential to influence sensitivity to phenological mismatch and, potentially, marine survival.
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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 | 0.000 |
| 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".