The impacts of extreme and fluctuating temperatures on trait‐mediated indirect aphid–parasitoid interactions
Bibliographic record
Abstract
1. Global climate change models predict an increase in the frequency and magnitude of extreme temperature events. These temperature events, heatwaves for example, will impact a wide range of physiological and behavioural processes, particularly in ectotherms, and may therefore influence interactions between species. 2. Anti‐predator responses may be more costly under more severe temperature regimes and therefore trait‐mediated disturbance could lead to high mortality or reduced reproduction under extreme and fluctuating temperature regimes. 3. We examined the impacts of extreme and fluctuating temperatures on trait‐mediated indirect interactions in an aphid–parasitoid community. 4. In treatments that isolated the effects of trait‐mediated disturbance from the effects of foraging parasitoids we found that an increase in both the amplitude and frequency of peak temperatures reduced aphid numbers and provided evidence that the cost of trait‐mediated disturbance could increase under frequent periods of high temperature. Aphid dispersal also increased with more frequent periods of high temperature. 5. In treatments where female wasps were allowed to freely forage (direct + trait‐mediated effects), there was no evidence that extreme and fluctuating temperatures influenced the wasp's foraging ability. Exposure to extreme fluctuating temperatures did not influence the offspring production of exposed wasps or the position of the mummies within the plots.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".