Benthic–limnetic morphological variation in fishes: Dissolved organic carbon concentration produces unexpected patterns
Bibliographic record
Abstract
Abstract Variation in traits related to foraging and locomotion in benthic and limnetic habitats has been observed in many fishes. Benthic and limnetic food chain productivity in lakes is strongly influenced by the concentration of dissolved organic carbon (DOC) in the water, suggesting that DOC might indirectly impose selection on these traits and lead to classic benthic forms at low DOC concentrations and limnetic forms at high DOC concentrations. We tested this hypothesis via geometric morphometric and meristic analyses of bluegill sunfish (Lepomis macrochirus, Centrarchidae) from 14 lakes with DOC concentrations ranging from 4 to 24 mg/L. These lakes, located in close proximity to each other, straddle the drainage divide between the Mississippi River and Laurentian Great Lakes basins in northern Wisconsin, USA. Bluegill morphology was consistently related to lake DOC concentration in both drainage basins, despite differences in morphology between basins. Fish from higher DOC lakes had deeper bodies and smaller heads, among other differences, though the proportion of shape variation described by DOC was low. Gill raker length and inter‐raker spacing were positively related to DOC concentration. Although some traits were thus related to DOC concentration, the directions of these relationships did not match the predicted benthic–limnetic patterns. Further, no relationships were evident between DOC and gill raker number, eye width, pectoral fin dimensions, or pectoral fin insertion angle in univariate analyses. These variable outcomes suggest that selection linked to DOC does not map neatly onto the classic benthic–limnetic axis, that high DOC favors a benthic–limnetic generalist rather than a limnetic specialist, or that the benthic–limnetic morphological dichotomy is less clear and universal than is often suggested.
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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 | 0.000 | 0.001 |
| 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".