Physiological Plasticity to Water Flow Habitat in the Damselfish, Acanthochromis polyacanthus: Linking Phenotype to Performance
Bibliographic record
Abstract
The relationships among animal form, function and performance are complex, and vary across environments. Therefore, it can be difficult to identify morphological and/or physiological traits responsible for enhancing performance in a given habitat. In fishes, differences in swimming performance across water flow gradients are related to morphological variation among and within species. However, physiological traits related to performance have been less well studied. We experimentally reared juvenile damselfish, Acanthochromis polyacanthus, under different water flow regimes to test 1) whether aspects of swimming physiology and morphology show plastic responses to water flow, 2) whether trait divergence correlates with swimming performance and 3) whether flow environment relates to performance differences observed in wild fish. We found that maximum metabolic rate, aerobic scope and blood haematocrit were higher in wave-reared fish compared to fish reared in low water flow. However, pectoral fin shape, which tends to correlate with sustained swimming performance, did not differ between rearing treatments or collection sites. Maximum metabolic rate was the best overall predictor of individual swimming performance; fin shape and fish total length were 3.3 and 3.7 times less likely than maximum metabolic rate to explain differences in critical swimming speed. Performance differences induced in fish reared in different flow environments were less pronounced than in wild fish but similar in direction. Our results suggest that exposure to water motion induces plastic physiological changes which enhance swimming performance in A. polyacanthus. Thus, functional relationships between fish morphology and performance across flow habitats should also consider differences in physiology.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".