Optimal temperature range of a plastic species,<i>Drosophila simulans</i>
Bibliographic record
Abstract
1. When a species experiences a new climate, it can adapt in two main ways: become genetically adapted to the new temperature, or adopt a plastic approach that allows it to survive at a range of temperatures. 2. The constraint on fitness for genetically adapted populations that are exposed to a new temperature has been well studied, but the range of optimal temperatures and their effect on fitness has never been examined across the worldwide distribution of a plastic species. 3. Here, we determined the optimum temperature range of 11 populations of the phenotypically plastic species Drosophila simulans. We measured the influence of temperature on eggs, larvae and adults at six temperatures that span the natural range the flies experience during their primary breeding season. We found no correlation between optimum temperature and native temperature, an effect that is not likely due to laboratory maintenance, suggesting that the species has not locally adapted to temperature. 4. We also found that this species had equal survival and reproductive success at most of the temperatures and life stages that we tested, regardless of the native temperature where the flies originated. 5. Thus, this genetically plastic species has an optimum fitness at a surprisingly wide range of temperatures, and is the first example of a cosmopolitan species exhibiting this large amount of plasticity across its sampling distribution.
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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".