Condition Dependence of a Multicomponent Sexual Display Trait in <i>Drosophila serrata</i>
Bibliographic record
Abstract
Theory predicts that costly sexual displays should evolve condition dependence if the marginal fitness gain from trait exaggeration is greater for high- than for low-condition individuals and that the strength of condition dependence should increase with the strength of directional selection. While there is substantial support for the first prediction, evidence for the latter is much weaker. We undertook a quantitative test of this prediction for a multivariate sexual display consisting of a suite of contact pheromones termed "cuticular hydrocarbons" (CHCs) in Drosophila serrata. We performed a dietary manipulation of condition (i.e., the pool of metabolic resources available for allocation to fitness-enhancing traits) within a half-sibling breeding design, thereby also providing insight into the genetic basis of condition dependence. As predicted, the linear combination of CHCs under the strongest sexual selection from female mate preferences was unusually condition dependent relative to other CHC combinations within the population ([Formula: see text]). A significant positive correlation also existed between the strengths of condition dependence and sexual selection among different CHC blends ([Formula: see text], [Formula: see text]). Finally, sires varied in their response to the dietary manipulation, demonstrating significant genetic variance in condition dependence. Our results are consistent with the evolution of heightened condition dependence of sexual displays in response to persistent sexual selection.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".