Overwintering drives rapid adaptation in <i>Drosophila</i> with potential costs to insecticide resistance
Bibliographic record
Abstract
Winter is a formidable challenge for ectotherms that inhabit temperate climates. The extent to which winter conditions drive rapid adaptation, and separately, how selection from novel stressors affects adaptation to winter, remain poorly understood. Here, we use replicate populations of Drosophila melanogaster in a field experiment to test (i) whether winter conditions drive rapid adaptation and (ii) for trade-offs between insecticide resistance and overwintering survival. Following a longitudinal field experiment investigating the evolution of insecticide resistance, we tracked subsequent evolution during an overwintering period. In unexposed control populations, we detected parallel evolutionary shifts indicative of adaptation to winter conditions in multiple traits, including body size and fecundity. Additionally, populations that had evolved insecticide resistance during the growing season were more likely to go extinct than control populations. Further, both control and resistant populations showed patterns of lower resistance following the winter period, suggestive of a trade-off between overwintering success and insecticide resistance. Rapid evolutionary responses to winter conditions, and potential costs of resistance, provide important context for understanding overwintering performance in temperate insects with implications for pest management and ecosystem services.
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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".