Multicoloured greenbeards, bacteriocin diversity and the rock‐paper‐scissors game
Bibliographic record
Abstract
Greenbeard genes identify copies of themselves in other individuals and cause their bearer to behave nepotistically towards those individuals. Bacterial toxins (bacteriocins) exemplify the greenbeard effect because producer strains carry closely linked genes for immunity, such that toxicity is limited to nonproducer strains. Bacteriocin producers can be maintained in a dynamic polymorphism, known as rock-paper-scissors (RPS) dynamics, with immune and susceptible strains. However, it is unclear whether and how such dynamics will be maintained in the presence of multiple toxin types (multiple beard 'colours'). Here, we analyse strain dynamics using models of recurrent patch colonization and population growth. We find that (i) polymorphism is promoted by a small number of founding lineages per patch, strong local resource competition and the occurrence of mutations; (ii) polymorphism can be static or dynamic, depending on the intensity of local interactions and the costs of toxins and immunity; (iii) the occurrence of multiple toxins can promote RPS dynamics; and (iv) strain diversity can be maintained even when toxins differ in toxicity or lineages can exhibit multitoxicity/multi-immunity. Overall, the factors that maintain simple RPS dynamics can also promote the coexistence of multiple toxin types (multiple beard colours), thus helping to explain the remarkable levels of bacteriocin diversity in nature. More generally, we contrast these results with the maintenance of marker diversity in genetic kin recognition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".