The developmental environment mediates adult seminal proteome allocation in male <i>Drosophila melanogaster</i>
Bibliographic record
Abstract
Abstract Early life conditions can have long-lasting fitness effects on organisms. In insects, crowding during larval stages impose a significant constraint on adult phenotypes due to increased intraspecific competition for resources, which can modulate males’ success in pre- and post-mating competition in adulthood. Evidence for larval crowding effects on male seminal fluid allocation exists but is limited to a small subset of well-known seminal fluid proteins (Sfps), and often overlooks the interactions between male and female phenotypes. We currently lack a comprehensive understanding of how male and female larval crowding interact to affect production, composition, and transfer of the wider seminal proteome. Here, we manipulated Drosophila melanogaster larval crowding (low versus high) of males and females to generate individuals with large and small body size, respectively. We mated individuals in a fully factorial design, and measured the abundance, composition, and transfer of Sfps. Large males produced Sfps in significantly higher abundances, yet this difference was marginal and not detected when Sfps were analysed individually. Conversely, small males transferred greater quantities of much of their seminal proteome to females than did large males. When analysing proteins individually, 10 Sfps were transferred in significantly higher abundances by small males than large males. Our results suggest that small males invest more on each mating opportunity, potentially as a response of overall fewer mating opportunities due to their reduced size, or due to the larval cues of high population density. This work provides an insight into early life effects on ejaculate allocation in D. melanogaster and sheds light on the physiological and behavioural responses to developmental conditions 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) | 0.002 | 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".