The role of resource defensibility in facilitating sexually selected weapon evolution: an experimental evolution test
Bibliographic record
Abstract
Animal weapons have evolved multiple times, primarily for battling for access to mates. Despite intra-sexual selection being common, exaggerated weapons have evolved relatively rarely. So why do exaggerated weapons not evolve more commonly? It has been hypothesized that three conditions are necessary for evolution of exaggerated weapons: high variance in reproductive success, patchy, high-value resources, and spatial environments conducive to one-on-one competition. Here, we test this hypothesis by performing experimental evolution in Drosophila melanogaster, utilizing heterogeneous environments where conditions facilitating territorial defense and opportunities for competitive interactions vary. We examine changes in sexually dimorphic morphology and male aggression that are predicted to occur, based on this model. We also examine whether condition dependence for sexual dimorphism has evolved after 35 and 75 generations of experimental evolution. Aggression did increase, albeit modestly, in environments that facilitate resource defense. Morphological changes are modest although with some trait-specific changes to allometry, generally in the opposite direction of our predictions. Condition dependence trends in the opposite direction from those predicted by our hypothesis as well. We discuss our results in the context of the necessary conditions for the evolution of exaggerated weapons, and if, and when condition dependence may evolve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".