Ecomorphological patterns linking morphology and diet across three populations of pumpkinseed sunfish (<i>Lepomis</i><i> gibbosus</i>)
Bibliographic record
Abstract
Relationships between morphological traits and their ecological function frequently result in patterns that are consistently observed within taxa. Across fishes, the field of ecomorphology has identified a number of morphological traits linked to foraging tactic. Here we examined the links between morphology and diet in pumpkinseed sunfish (Lepomis gibbosus (L., 1758)) from three temperate freshwater lakes. We focused on morphological variation in pharyngeal jaw and gill raker structures, both involved with the processing of prey after capture, in relation to diet. Using stomach contents and stable isotope analysis, we established mean resource use estimates for both populations and individual fish. Among populations and individuals within lakes, we observed that pharyngeal jaw size and gill raker spacing increased with the consumption of littoral prey (e.g., hard-shelled snails) relative to pelagic prey (e.g., zooplankton), but the morphological changes were greater for the pharyngeal jaws. Overall, the relationships that we observed between morphology and foraging tactic were consistent with patterns observed in pumpkinseed and across other fishes. Individual-level diet variation associated with morphology may result in phenotypic diversity within populations that has multiple ecological and evolutionary implications for these populations.
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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 | 0.000 |
| 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".