Temperature and its impact on predation risk within aquatic ecosystems
Bibliographic record
Abstract
Metabolic rates of fish and their activity levels have thermal optima. When environmental temperatures are below these optima, increasing temperature will increase their rates of energy consumption, resulting in a corresponding increase in the risk of starvation. For that reason we predicted that within this temperature range, food is of greater value at higher temperatures so fish should be willing to incur greater costs to obtain it. To test this hypothesis, we measured how the activity and foraging rates of the fathead minnow (Pimephales promelas) changed with temperature at 4, 15, and 24 °C. As expected, fish activity and foraging were greater at higher temperatures. We then measured the impact of predation risk on foraging decisions at 5, 15, and 23 °C. At 5 and 15 °C, the risk of predation had a significant effect on foraging decisions, but there was no effect at 23 °C. These results demonstrate that increasing temperatures below their optimal level diminish the impact of predation risk on foraging behaviour and may mean that the direct consumptive effect of predators on aquatic communities will be greater at warmer temperatures while the risk of predation will become a less important factor, and vice versa.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".