Evolutionary divergence in phenotypic plasticity shapes brain size variation between coexisting sunfish ecotypes
Bibliographic record
Abstract
Mechanisms that generate brain size variation and the consequences of such variation on ecological performance are poorly understood in most natural animal populations. We use a reciprocal-transplant common garden experiment and foraging performance trials to test for brain size plasticity and the functional consequences of brain size variation in Pumpkinseed sunfish (Lepomis gibbosus) ecotypes that have diverged between nearshore littoral and offshore pelagic lake habitats. Different age-classes of wild-caught juveniles from both habitats were exposed for 6 months to treatments that mimicked littoral and pelagic foraging. Plastic responses in oral jaw size suggested that treatments mimicked natural habitat-specific foraging conditions. Plastic brain size responses to foraging manipulations differed between ecotypes, as only pelagic sourced fish showed brain size plasticity. Only pelagic juveniles under 1 year-old expressed this plastic response, suggesting that plastic brain size responses decline with age and so may be irreversible. Finally, larger brain size was associated with enhanced foraging performance on live benthic but not pelagic prey, providing the first experimental evidence of a relationship between brain size and prey-specific foraging performance in fishes. The recent post-glacial origin of these ecotypes suggests that brain size plasticity can rapidly evolve and diverge in fish under contrasting ecological conditions.
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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 | 0.001 |
| 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".