Methods matter: considering locomotory mode and respirometry technique when estimating metabolic rates of fishes
Bibliographic record
Abstract
Respirometry is frequently used to estimate metabolic rates and examine organismal responses to environmental change. Although a range of methodologies exists, it remains unclear whether differences in chamber design and exercise (type and duration) produce comparable results within individuals and whether the most appropriate method differs across taxa. We used a repeated-measures design to compare estimates of maximal and standard metabolic rates (MMR and SMR) in four coral reef fish species using the following three methods: (i) prolonged swimming in a traditional swimming respirometer; (ii) short-duration exhaustive chase with air exposure followed by resting respirometry; and (iii) short-duration exhaustive swimming in a circular chamber. We chose species that are steady/prolonged swimmers, using either a body-caudal fin or a median-paired fin swimming mode during routine swimming. Individual MMR estimates differed significantly depending on the method used. Swimming respirometry consistently provided the best (i.e. highest) estimate of MMR in all four species irrespective of swimming mode. Both short-duration protocols (exhaustive chase and swimming in a circular chamber) produced similar MMR estimates, which were up to 38% lower than those obtained during prolonged swimming. Furthermore, underestimates were not consistent across swimming modes or species, indicating that a general correction factor cannot be used. However, SMR estimates (upon recovery from both of the exhausting swimming methods) were consistent across both short-duration methods. Given the increasing use of metabolic data to assess organismal responses to environmental stressors, we recommend carefully considering respirometry protocols before experimentation. Specifically, results should not readily be compared across methods; discrepancies could result in misinterpretation of MMR and aerobic scope.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".