Convergent polymorphism between stream and lake habitats: the case of brook char
Bibliographic record
Abstract
Phenotypic variability represents an important factor allowing species to adapt to local environmental conditions, but mechanisms underlying such variation are incompletely understood. This study investigated whether habitat-specific demands on swimming performance or difference in trophic relationships in lakes (pelagic, littoral) and streams (riffle, pool) were significant predictors of phenotypic variation exhibited by brook char (Salvelinus fontinalis), the only fish in the study habitats. Specifically, we hypothesized that pelagic and riffle habitats would impose greater selective pressures associated with swimming, resulting in body morphologies that were dorsoventrally compressed, anterior–posteriorly elongated, and that exhibited a long, narrow caudal peduncle. Geometric morphometrics was applied in a quantitative analysis of body morphology among habitats, whereas stable isotope analysis was used to differentiate between food sources. Analyses revealed that while body morphology differed between lake and stream habitats, there was convergence between the pelagic and riffle habitats, as well as among littoral and riffle and pool environments. The littoral and pool habitats were thought to be more structurally complex, thereby selecting for increased maneuverability but lower sustained swimming and correspondingly deeper bodies with shorter, dorsoventrally expanded caudal peduncles. Carbon source and trophic position did not differ among habitats with a system, suggesting that feeding was not the main influence on morphological plasticity; however, fish in the stream were feeding at a higher trophic position than fish in the lake. These findings suggest that individual species may take advantage of morphological variation to better adapt local surroundings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".