Pacific Salmon in Hot Water: Applying Aerobic Scope Models and Biotelemetry to Predict the Success of Spawning Migrations
Bibliographic record
Abstract
Concern over global climate change is widespread, but quantifying relationships between temperature change and animal fitness has been a challenge for scientists. Our approach to this challenge was to study migratory Pacific salmon (Oncorhynchus spp.), fish whose lifetime fitness hinges on a once-in-a-lifetime river migration to natal spawning grounds. Here, we suggest that their thermal optimum for aerobic scope is adaptive for river migration at the population level. We base this suggestion on several lines of evidence. The theoretical line of evidence comes from a direct association between the temperature optimum for aerobic metabolic scope and the temperatures historically experienced by three Fraser River salmon populations during their river migration. This close association was then used to predict that the occurrence of a period of anomalously high river temperatures in 2004 led to a complete collapse of aerobic scope during river migration for a portion of one of the sockeye salmon (Oncorhynchus nerka) populations. This prediction was corroborated with empirical data from our biotelemetry studies, which tracked the migration of individual sockeye salmon in the Fraser River and revealed that the success of river migration for the same sockeye population was temperature dependent. Therefore, we suggest that collapse of aerobic scope was an important mechanism to explain the high salmon mortality observed during their migration. Consequently, models based on thermal optima for aerobic scope for ectothermic animals should improve predictions of population fitness under future climate scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".