Pollination efficiency and the evolution of sex allocation – diminishing returns matter
Bibliographic record
Abstract
Immobility of flowering plants requires them to engage pollen vectors to outcross, introducing considerable inefficiency in the conversion of pollen production into sired seeds. Whether inefficiencies influence the evolution of the relative resource allocation to female and male functions has been debated for more than 40 years. Whereas early models suggested no effect, negative interspecific relations of mean pollen production and pollen : ovule ratios to the proportion of removed pollen that is exported to stigmas (pollen-transfer efficiency) indicate otherwise. Here, we consider theoretically a key condition that determines whether the efficiencies of processes (first derivative of process output with respect to input) affect the evolutionarily stable sex (ESS) allocation. No effect arises if all individuals experience the same efficiency. By contrast, a decline in process efficiency with increasing allocation (diminishing returns) generally reduces the ESS male allocation for a population. Furthermore, differences in the allocation dependence of efficiencies (and hence the ESS sex allocation) among populations/species create a negative relation of realised efficiency to male allocation among species, like that observed empirically. Diminishing returns arise for various processes that affect siring (e.g. pollen export and local pollen competition to fertilise ovules), which may differ in their relative influence on sex allocation among species.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".