Morphology and Efficiency of a Specialized Foraging Behavior, Sediment Sifting, in Neotropical Cichlid Fishes
Bibliographic record
Abstract
Understanding of relationships between morphology and ecological performance can help to reveal how natural selection drives biological diversification. We investigate relationships between feeding behavior, foraging performance and morphology within a diverse group of teleost fishes, and examine the extent to which associations can be explained by evolutionary relatedness. Morphological adaptation associated with sediment sifting was examined using a phylogenetic linear discriminant analysis on a set of ecomorphological traits from 27 species of Neotropical cichlids. For most sifting taxa, feeding behavior could be effectively predicted by a linear discriminant function of ecomorphology across multiple clades of sediment sifters, and this pattern could not be explained by shared evolutionary history alone. Additionally, we tested foraging efficiency in seven Neotropical cichlid species, five of which are specialized benthic feeders with differing head morphology. Efficiency was evaluated based on the degree to which invertebrate prey could be retrieved at different depths of sediment. Feeding performance was compared both with respect to feeding mode and species using a phylogenetic ANCOVA, with substrate depth as a covariate. Benthic foraging performance was constant across sediment depths in non-sifters but declined with depth in sifters. The non-sifting Hypsophrys used sweeping motions of the body and fins to excavate large pits to uncover prey; this tactic was more efficient for consuming deeply buried invertebrates than observed among sediment sifters. Findings indicate that similar feeding performance among sediment-sifting cichlids extracting invertebrate prey from shallow sediment layers reflects constraints associated with functional morphology and, to a lesser extent, phylogeny.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".