Basal cold but not heat tolerance constrains plasticity among Drosophila species (Diptera: Drosophilidae)
Bibliographic record
Abstract
Thermal tolerance and its plasticity are important for understanding ectotherm responses to climate change. However, it is unclear whether plasticity is traded-off at the expense of basal thermal tolerance and whether plasticity is subject to phylogenetic constraints. Here, we investigated associations between basal thermal tolerance and acute plasticity thereof in laboratory-reared adult males of eighteen Drosophila species at low and high temperatures. We determined the high and low temperatures where 90% of flies are killed (ULT(90) and LLT(90) , respectively) and also the magnitude of plasticity of acute thermal pretreatments (i.e. rapid cold- and heat-hardening) using a standardized, species-specific approach for the induction of hardening responses. Regression analyses of survival variation were conducted in ordinary and phylogenetically informed approaches. Low-temperature pretreatments significantly improved LLT(90) in all species tested except for D. pseudoobscura, D. mojavensis and D. borealis. High-temperature pretreatment only significantly increased ULT(90) in D. melanogaster, D. simulans, D. pseudoobscura and D. persimilis. LLT(90) was negatively correlated with low-temperature plasticity even after phylogeny was accounted for. No correlations were found between ULT(90) and LLT(90) or between ULT(90) and rapid heat-hardening (RHH) in ordinary regression approaches. However, after phylogenetic adjustment, there was a positive correlation between ULT(90) and RHH. These results suggest a trade-off between basal low-temperature tolerance and acute low-temperature plasticity, but at high temperatures, increased basal tolerance was accompanied by increased plasticity. Furthermore, high- and low-temperature tolerances and their plasticity are clearly decoupled. These results are of broad significance to understanding how organisms respond to changes in habitat temperature and the degree to which they can adjust thermal sensitivity.
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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".