The Origin of Gender Dimorphism in Animal‐Dispersed Plants: Disruptive Selection in a Model of Social Evolution
Bibliographic record
Abstract
Dioecy (separate sexes) in plants is associated with animal fruit dispersal, but hypotheses for a role of dispersal in the origin of gender dimorphism have received little support. Here, I present a patch-structured model to explore the conditions that favor dimorphism when dispersal is coupled with sex allocation. The model shows that if the proportion of fruits dispersed from a cosexual plant increases with its allocation to fruits (causing accelerating fitness returns from dispersed fruits), disruptive selection can arise when the cost of dispersal is minimal and the correlation among patchmates (i.e., relatedness) is high. In reality, however, the proportion of fruits dispersed from a plant's patch may decline with further allocation to fruits. Even in this case, novel contexts that lead to disruptive selection on sex allocation are discovered, occurring when dispersal costs are high and relatedness is low, which causes accelerating returns from nondispersed fruits. Hence, surprisingly, gender dimorphism can evolve because female specialists are better able to escape local competition or to succeed in it. Building on the few existing models of disruptive selection on social traits, the mechanisms here show that selection for relaxed local competition (cooperation) can sometimes facilitate diversification and sometimes prevent it.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".