The Developmental Environment Mediates Adult Seminal Proteome Allocation in Male <i>Drosophila melanogaster</i>
Bibliographic record
Abstract
Early life conditions can have long-lasting effects on fitness. In insects, larval crowding increases intraspecific competition, shaping adult phenotypes and influencing male reproductive success in pre- and post-mating competition. Although some larval crowding effects on seminal fluid protein (Sfp) allocation are known, studies often focus on a small subset of Sfps and overlook male-female interactions. A comprehensive understanding of how male and female larval environments interact to influence seminal proteome composition and transfer is still lacking. Here, we manipulated Drosophila melanogaster larval crowding (low vs. high) to generate large and small adults and mated individuals in a fully factorial design. We then measured Sfp production, composition and transfer. Large males produced relatively higher quantities of Sfps. However, small males transferred greater quantities of a subset of Sfps. When examining proteins individually, 10 Sfps were transferred at significantly higher abundances by small males than large males. Our findings suggest that small males invest more per mating, potentially due to fewer mating opportunities or cues of high larval density influencing reproductive strategies. This study provides new insights into early life effects on ejaculate allocation in D. melanogaster, highlighting physiological and behavioural responses to developmental conditions. Understanding these mechanisms offers valuable perspectives on reproductive strategies and fitness trade-offs in insects.
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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".