What do warming waters mean for fish physiology and fisheries?
Bibliographic record
Abstract
Environmental signals act primarily on physiological systems, which then influence higher-level functions such as movement patterns and population dynamics. Increases in average temperature and temperature variability associated with global climate change are likely to have strong effects on fish physiology and thereby on populations and fisheries. Here we review the principal mechanisms that transduce temperature signals and the physiological responses to those signals in fish. Temperature has a direct, thermodynamic effect on biochemical reaction rates. Nonetheless, plastic responses to longer-term thermal signals mean that fishes can modulate their acute thermal responses to compensate at least partially for thermodynamic effects. Energetics are particularly relevant for growth and movement, and therefore for fisheries, and temperature can have pronounced effects on energy metabolism. All energy (ATP) production is ultimately linked to mitochondria, and temperature has pronounced effects on mitochondrial efficiency and maximal capacities. Mitochondria are dependent on oxygen as the ultimate electron acceptor so that cardiovascular function and oxygen delivery link environmental inputs with energy metabolism. Growth efficiency, that is the conversion of food into tissue, changes with temperature, and there are indications that warmer water leads to decreased conversion efficiencies. Moreover, movement and migration of fish relies on muscle function, which is partially dependent on ATP production but also on intracellular calcium cycling within the myocyte. Neuroendocrine processes link environmental signals to regulated responses at the level of different tissues, including muscle. These physiological processes within individuals can scale up to population responses to climate change. A mechanistic understanding of thermal responses is essential to predict the vulnerability of species and populations to climate change.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".