Rethinking Mutualism Stability: Cheaters and the Evolution of Sanctions
Bibliographic record
Abstract
How cooperation originates and persists in diverse species, from bacteria to multicellular organisms to human societies, is a major question in evolutionary biology. A large literature asks: what prevents selection for cheating within cooperative lineages? In mutualisms, or cooperative interactions between species, feedback between partners often aligns their fitness interests, such that cooperative symbionts receive more benefits from their hosts than uncooperative symbionts. But how do these feedbacks evolve? Cheaters might invade symbiont populations and select for hosts that preferentially reward or associate with cooperators (often termed sanctions or partner choice); hosts might adapt to variation in symbiont quality that does not amount to cheating (e.g., environmental variation); or conditional host responses might exist before cheaters do, making mutualisms stable from the outset. I review evidence from yucca-yucca moth, fig-fig wasp, and legume-rhizobium mutualisms, which are commonly cited as mutualisms stabilized by sanctions. Based on the empirical evidence, it is doubtful that cheaters select for host sanctions in these systems; cheaters are too uncommon. Recognizing that sanctions likely evolved for functions other than retaliation against cheaters offers many insights about mutualism coevolution, and about why mutualism evolves in only some lineages of potential hosts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".