An energetics–performance framework for wild fishes
Bibliographic record
Abstract
There is growing evidence that bioenergetics can explain relationships between environmental conditions and fish behaviour, distribution and fitness. Fish energetic needs increase predictably with water temperature, but metabolic performance (i.e., aerobic scope) exhibits varied relationships, and there is debate about its role in shaping fish ecology. Here we present an energetics-performance framework, which posits that ecological context determines whether energy expenditure or metabolic performance influence fish behaviour and fitness. From this framework, we present testable predictions about how temperature-driven variability in energetic demands and metabolic performance interact with ecological conditions to influence fish behaviour, distribution and fitness. Specifically, factors such as prey availability and the spatial distributions of prey and predators may alter fish temperature selection relative to metabolic and energetic optima. Furthermore, metabolic flexibility is a key determinant of how fish will respond to changing conditions, such as those predicted with climate change. With few exceptions, these predictions have rarely been tested in the wild due partly to difficulties in remotely measuring aspects of fish energetics. However, with recent advances in technology and measurement techniques, we now have a better capacity to measure bioenergetics parameters in the wild. Testing these predictions will provide a more mechanistic understanding of how ecological factors affect fish fitness and population dynamics, advancing our knowledge of how species and ecosystems will respond to rapidly changing environments.
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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) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".