The role of the unicellular bottleneck and organism size in mediating cooperation and conflict among cells at the onset of multicellularity
Bibliographic record
Abstract
Abstract Evolutionary transitions in individuality introduce new levels of selection and thus enable discordant selection, threatening the stability of the transition. Cheating is such a problem for multicellularity. So why have so many transitions to multicellularity persisted? One possibility is that the unicellular propagule maintains cooperation among cells by purging cheaters. The evolution of propagule size has been modeled previously, but in the absence of competition between individuals, which may often select for larger propagules. How does the nature of competition between individuals affect the optimal propagule size in the presence of cheating? Here we take a model of early multicellularity, add phenotypic switching between cheating and cooperative phenotypes, and simulate size-dependent competition on a lattice, which allows us to tune the strength of interspecific vs. intraspecific competition via dispersal. As expected, cheating favors strategies with unicellular propagules while size-dependent competition favors strategies with few large propagules (binary fragmentation). How these opposing forces resolve depends on dispersal. Local dispersal, which intensifies intraspecific competition, favors binary fragmentation, which reduces intraspecific competition for space, with one unicellular propagule. Global dispersal instead favours multiple fission when cheating is common. We also find that selfishness promotes smaller body size, despite direct opposing selection from competition. Our results shed light on the evolution of multicellular life cycles and the prevalence of a unicellular stage in the multicellular life cycle across the tree of life. Author summary A multicellular organism is a group of cooperating cells. But wherever there is cooperation there is the temptation to cheat. Having offspring that start as a single cell (a unicellular bottleneck) has been hypothesized as an adaptation to purge lineages of cheating cells. We model the evolution of offspring size but add competition between individuals, which may select against small unicellular offspring. We find that having some unicellular offspring is still a successful strategy, but how many depends on the nature of competition.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".