Eusociality influences the strength of negative selection on insect genomes
Bibliographic record
Abstract
While much of the focus of sociobiology concerns identifying genomic changes that influence social behaviour, we know little about the consequences of social behaviour on genome evolution. It has been hypothesized that social evolution can influence the strength of negative selection via two mechanisms. First, division of labour can influence the efficiency of negative selection in a caste-specific manner; indirect negative selection on worker traits is theoretically expected to be weaker than direct selection on queen traits. Second, increasing social complexity is expected to lead to relaxed negative selection because of its influence on effective population size. We tested these two hypotheses by estimating the strength of negative selection in honeybees, bumblebees, paper wasps, fire ants and six other insects that span the range of social complexity. We found no consistent evidence that negative selection was significantly stronger on queen-biased genes relative to worker-biased genes. However, we found strong evidence that increased social complexity reduced the efficiency of negative selection. Our study clearly illustrates how changes in behaviour can influence patterns of genome evolution by modulating the strength of natural selection.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".