Morphological predictors of swimming speed: a case study of pre-settlement juvenile coral reef fishes
Bibliographic record
Abstract
The swimming abilities of fishes are of vital importance to their ecology, and studies on fish swimming have been the focus of research for over a century. Here we explore the relationship between swimming ability and external body morphology, using data on U(crit) swimming speeds of 100 species of pre-settlement juvenile coral reef fishes (at the transition between the larval and adult habitats), comprising 26 different families from 5 orders. The taxonomic diversity of this methodologically consistent dataset provides a unique opportunity to examine the relationship between form and function in fish swimming across a broad taxonomic range. Overall, we found that a predictive model incorporating total length (TL), the square of caudal peduncle depth factor (CPDF(2)) and aspect ratio (AR) can be used to accurately predict swimming performance of a wide range of fish families, and was able to explain 69% of the variability in swimming performance of these pre-settlement juvenile fishes. The model was also able to successfully predict the swimming speed of an out-group salmonid species (Oncorhynchus mykiss). There was no evidence that the model fit differed among taxonomic groups, despite the inclusion of five different orders of fishes, suggesting that body morphology sufficiently explains the bulk of differences in swimming performance. Furthermore, the model appears to work equally well for fishes from the Great Barrier Reef and the Caribbean, and for families with different adult habitat associations and swimming modes. It remains to be determined how well the model predicts the swimming abilities of temperate species as well as adults of these same species. This model provides an invaluable means of predicting swimming abilities of pre-settlement juvenile fishes that are unable to be reared in the laboratory, do not perform well in swimming flumes or are unable to be captured live in the field.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".