Predation shapes behavioral lateralization: insights from an adaptive radiation of livebearing fish
Bibliographic record
Abstract
AbstractHemispheric brain lateralization can drive the expression of behavioral asymmetry, or laterality, which varies notably both within and among species. To explain these left–right behavioral asymmetries in animals, predator-mediated selection is often invoked. Recent studies have revealed that a relatively high degree of lateralization correlates positively with traits known to confer survival benefits against predators, including escape performance, multitasking abilities, and group coordination. Yet, we still know comparatively little about 1) how consistently predators shape behavioral lateralization, 2) the importance of sex-specific variation, and 3) the degree to which behavioral lateralization is heritable. Here, we take advantage of the model system of the radiation of Bahamas mosquitofish (Gambusia hubbsi) and measure behavioral lateralization in hundreds of wild fish originating from multiple blue holes that differ in natural predation pressure. Moreover, we estimated the heritability of this trait using laboratory-born fish from one focal population. We found that the degree of lateralization but not the particular direction of lateralization (left or right) differed significantly across high and low predation risk environments. Fish originating from high-predation environments were more strongly lateralized, especially females. We further confirmed a genetic basis to behavioral lateralization in this species, with significant additive genetic variation in the population examined. Our results reveal that predation risk represents one key ecological factor that has likely shaped the origin and maintenance of this widespread behavioral phenomenon, even potentially explaining some of the sex-specific patterns of laterality recently described in some animals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".