Appropriate evaluation of rapid evolutionary responses to seasonal environmental variability in a wild Drosophila population
Bibliographic record
Abstract
Abstract Seasonal environmental change is one of the most rapid and striking environmental variables. Although relatively rapid adaptation to environmental changes over several years or several decades has been described in many taxa, rapid responses to seasonal environments are delicate, and therefore, the detection of the evolutionary responses requires sensitive methods. Here, we examined the evolutionary response to seasonal environmental changes in terms of thermal tolerance and morphological traits using Drosophila lutescens collected at the spring and autumn periods in a single location. We first demonstrated that flies in the two seasonal periods were almost genetically identical using double-digest restriction site-associated DNA sequencing and analysis. With an experimental design that eliminated the effect of possible confounding environmental factors that influence thermal tolerance, we showed that the heat tolerance of D. lutescens was significantly higher in the autumn than in the spring. Furthermore, cold tolerance was slightly higher in the spring than in the autumn. Although wing length and thorax length did not change significantly between seasons, the ratio of wing length to thorax length changed significantly between them. These results indicate that seasonal environmental heterogeneity can induce evolution within a year. The present study demonstrates rapid evolutionary responses to environmental fluctuations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".