Exposure to predators does not lead to the evolution of larger brains in experimental populations of threespine stickleback
Bibliographic record
Abstract
Natural selection is often invoked to explain differences in brain size among vertebrates. However, the particular agents of selection that shape brain size variation remain obscure. Recent studies suggest that predators may select for larger brains because increased cognitive and sensory abilities allow prey to better elude predators. Yet, there is little direct evidence that exposure to predators causes the evolution of larger brains in prey species. We experimentally tested this prediction by exposing families of 1000-2000 F2 hybrid benthic-limnetic threespine stickleback to predators under naturalistic conditions, along with matched controls. After two generations of selection, we found that fish from the predator addition treatment had significantly smaller brains (specifically smaller telencephalons and optic lobes) than fish from the control treatment. After an additional generation of selection, we reared experimental fish in a common environment and found that this difference in brain size was maintained in the offspring of fish from the predator addition treatment. Our results provide direct experimental evidence that (a) predators can indeed drive the evolution of brain size--but not in the fashion commonly expected and (b) that the tools of experimental evolution can be used to the study the evolution of the vertebrate brain.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".