Intraspecific variation in metabolic responses of a cool water fish to increasing temperatures
Bibliographic record
Abstract
Growing impacts of climate change necessitate predicting species’ vulnerability to altered ecosystems. Assessing vulnerability requires understanding how species’ physiology, life history, and ecology vary among populations and can be altered by behavioral, plastic, and evolutionary adaptations. To examine intraspecific variation in sensitivity to climate change, we measured metabolic responses to acute and chronic temperature exposures in three rearing pond populations of walleye ( Sander vitreus), a cool-water-adapted fish species threatened by climate change. We show significant differences among rearing pond populations in response to increasing temperatures which may originate from broodstock, developmental plasticity, and acclimation. Our results indicate northern walleye may be more tolerant of acute and chronic exposure to higher temperatures by being able to maintain a higher aerobic scope than more southern populations. Furthermore, even over small geographic distances, populations can have significantly different physiological responses to environmental stressors. Quantifying variation in population-specific metabolic responses can inform predictions of growth, reproduction, and fitness across a species range and clarify the importance of within-species diversity in determining vulnerability to environmental stressors.
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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".