Identifying a causal agent of sexual selection on weaponry in an insect
Bibliographic record
Abstract
In many animal species, males do not seek females directly but instead locate and defend sites that contain spatially or temporally limited resources essential to female survival and reproduction. Resident males that successfully repel conspecific rivals can mate with females attracted to these resources. In theory, increasing resource value increases harem size and thus increases the opportunity (Imates) for and strength of sexual selection on traits crucial to male resource-holding potential and mating success. I experimentally tested this hypothesis in the field using the Wellington tree weta, Hemideina crassidens (Orthoptera: Tettigonioidea: Anostostomatidae), a sexually dimorphic insect in which males use their enlarged mandibles as weapons in male–male contests over access to females sheltering in tree cavities (galleries). By manipulating gallery size, I showed that, compared with smaller galleries, larger galleries housed larger harems. Variation in gallery size was an important determinant of Imates, but contrary to expectation, greater opportunity existed in small galleries compared with large galleries. As predicted, male weapon size was under stronger directional selection in large galleries because the fitness benefits were greater under these conditions compared with small galleries. My results help explain the positive association between average weapon size and average gallery size observed within and among tree weta populations in New Zealand.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".