Nutritional developmental history and its consequences for reproductive success in <i>Drosophila suzukii</i> (Diptera: Drosophilidae)
Bibliographic record
Abstract
Abstract The characteristics of the juvenile developmental environment of an individual can have many important consequences for their adult reproductive success as it may shape the development and expression of phenotypes that are relevant to the later operation of sexual selection. Drosophila suzukii (Matsumura) (Diptera: Drosophilidae) is an economically important invasive pest species that lays its eggs in many types of soft fruits and potentially experiences large intrapopulation spatial and temporal variation in its nutritional developmental environments. Here, we examine whether the larval nutritional developmental environment influences D. suzukii mate choice, egg production, and offspring performance. Using D. suzukii raised on diets differing in their nutritional quality, we examined mating preferences, fecundity, and offspring survivorship in “no-choice,” “female choice,” and “male choice” reproductive contexts. We found evidence for both adaptive and nonadaptive mate choice behaviours associated with the phenotypes of D. suzukii that had developed in different nutritional environments. These results reveal the complex nature of the relationship between the developmental environment and individual reproductive success in D. suzukii, which has important potential implications for future management plans involving this species.
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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".