Measuring maximum heart rate to study cardiac thermal performance and heat tolerance in fishes
Bibliographic record
Abstract
The thermal sensitivity of heart rate (fH) in fishes has fascinated comparative physiologists for well over a century. We now know that elevating fH is the primary mechanism through which fishes increase convective oxygen delivery during warming to meet the concomitant rise in tissue oxygen consumption. Thus, limits on fH can constrain whole-animal aerobic metabolism. In this Review, we discuss an increasingly popular methodology to study these limits, the measurement of pharmacologically induced maximum fH (fH,max) during acute warming of an anaesthetized fish. During acute warming, fH,max increases exponentially over moderate temperatures (Q10∼2-3), but this response is blunted with further warming (Q10∼1-2), with fH,max ultimately reaching a peak (Q10≤1) and the heartbeat becoming arrhythmic. Because the temperatures at which these transitions occur commonly align with whole-animal optimum and critical temperatures (e.g. aerobic scope and the critical thermal maximum), they can be valuable indicators of thermal performance. The method can be performed simultaneously on multiple individuals over a few hours and across a broad size range (<1 to >6000 g) with compact equipment. This simplicity and high throughput make it tractable in lab and field settings and enable large experimental designs that would otherwise be impractical. As with all reductionist approaches, the method does have limitations. Namely, it requires anaesthesia and pharmacological removal of extrinsic cardiac regulation. Nonetheless, the method has proven particularly effective in the study of patterns and limits of thermal plasticity and holds promise for helping to predict and mitigate outcomes of environmental 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".