Sex allocation and the evolution of insemination capacity under local mate competition
Bibliographic record
Abstract
Abstract Local mate competition (LMC) theory has proved enormously successful in predicting sex ratios across a broad range of organisms when localised mating patches lead to mating competition amongst kin. As such, LMC is a key component of sex allocation theory. However, the mating systems that influence and promote LMC also shape other traits, as well as sex allocation. These aspects of LMC mating systems have received far less attention, including in species where LMC is common, such as parasitoid wasps. Here, we consider how LMC influences the evolution of insemination capacity in parasitoids, a key reproductive allocation decision for males that should be under both natural and sexual selection. Basic LMC theory predicts that a single female exploiting a patch should produce just enough sons to inseminate all her daughters, that is, between them these sons should have sufficient insemination capacity to inseminate their sisters. However, the insemination capacity of males is generally higher than predicted and, in order to classify parasitoid species, we propose an Index of Insemination Strategy (IIS): the ratio between the insemination capacity of males on the emergence patch and the average number of females available per male at emergence on that patch. A survey of IIS for 25 species belonging to 10 hymenopteran families showed that IIS values ranged from 0.9 to 40.9, supporting the idea that males typically have more sperm than predicted. Several factors could explain these high IIS values, including non‐local mating, temporal variation in emergence, variation in mate acquisition capacity, the intensity of sperm competition, and responses to host quality.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".