Comparative dynamics of suction feeding in marine and freshwater three-spined stickleback, Gasterosteus aculeatus: kinematics and geometric morphometrics
Bibliographic record
Abstract
Locomotion and feeding are key axes of diversity among fishes, and these are commonly integrated for successful prey capture. However, little is known about biomechanical variation among highly ecologically divergent populations of fishes. Three-spined stickleback, Gasterosteus aculeatus, is an ideal species for teasing apart the relationships between ecology, form and function, given the numerous independent invasions of freshwater lakes and streams from a marine ancestor. These natural replicates afford the opportunity to isolate convergence and/or many-to-one mapping of form to function. To explore the divergence between marine and freshwater stickleback, we investigated the differences in suction feeding kinematics and ram speed among individuals from replicates of both habitats. Feeding sequences were obtained using a high-speed camera, and microcomputed tomography was used to assess three-dimensional geometric morphometrics. Prey capture kinematics of marine and freshwater stickleback differed significantly, with marine individuals consistently exhibiting faster ram speeds, larger gapes, more jaw protrusion, and greater magnitudes and speeds of cranial rotation. These are consistent with an attack strategy aimed at evasive prey. In addition, populations exhibited significant morphological divergence, but not always along the marine–freshwater axis of divergence. Thus, there appears to be a mismatch between morphology and function. Together, our approach has revealed the dynamic kinematic divergence of marine and freshwater stickleback and how functional studies can be used to predict ecology.
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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 | 0.000 |
| 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".