Migration and Spawning Affect the Stable Isotope Values of Multiple Tissues in Pacific Salmon
Bibliographic record
Abstract
Migration can be energetically demanding for animals, especially when individuals have only one chance to reproduce and rely on stored energy to complete both tasks. We investigated whether protein and fat catabolism, measured by stable isotope values, predicted successful migration and reproduction in semelparous sockeye salmon (Oncorhynchus nerka) in the Fraser River, British Columbia. We used stable isotope values of carbon (δ13C) and nitrogen (δ15N) from adipose fins, blood, and scales sampled upon initial capture to assess an individual’s oceanic habitat use; used passive integrated transponders to measure migration timing and success; and then collected isotope samples from the same individuals upon death to assess the level of protein and fat catabolism. We also assessed catabolism in pink salmon (Oncorhynchus gorbuscha) using stable isotope values from scales and adipose fins collected at death. We found consistent increases in δ13C over time across sockeye salmon tissues, showing that δ13C values collected from dead fish no longer represent ocean conditions. In contrast, δ15N increased only in adipose tissue of sockeye males and was particularly high in large male pink salmon, likely because of their extreme morphological changes for spawning. Migration time through lakes was related to δ13C, suggesting that males with lower energy reserves spent less time in lakes before spawning, and successful female sockeye spawners had higher δ13C values, suggesting that they catabolized more fat than unsuccessful females. Even though we were unable to link ocean habitat use to migration or reproductive success, we found several patterns of isotopic increases due to protein and lipid catabolism. These findings have implications for reinterpreting past and future studies using stable isotope values collected from migrating or dead salmon and, by extension, other animals.
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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) | 0.001 | 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".