Haplodiploidy and the Evolution of Eusociality: Worker Reproduction
Bibliographic record
Abstract
Hamilton suggested that inflated relatedness between sisters promotes the evolution of eusociality in haplodiploid populations. Trivers and Hare observed that for this to occur, workers have to direct helping preferentially toward the production of sisters. Building on this, they proposed two biological scenarios whereby haplodiploidy could act to promote the evolution of eusociality: (a) workers biasing the sex allocation of the queen's brood toward females and (b) workers replacing the queen's sons with their own sons. This "worker revolution," whereby the worker class seizes control of sex allocation and reproduction, is expected to lead to helping being promoted in worker-controlled colonies. Here, we use a kin-selection approach to model the two scenarios suggested by Trivers and Hare. We show that (1) worker control of sex allocation may promote helping, but this effect is likely to be weak and short lived; and (2) worker reproduction tends to inhibit rather than promote helping. Furthermore, the promotion of helping is reduced by a number of biologically likely factors, including the presence of workers increasing colony productivity, workers being unmated, and worker control of sex allocation being underpinned by many loci each having a small effect. Overall, our results suggest that haplodiploidy has had a negligible influence on the evolution of eusociality.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".