Effects of simulated heat waves on an experimental plant–herbivore–predator food chain
Bibliographic record
Abstract
Greater climatic variability and extreme climatic events are currently emerging as two of the most important facets of climate change. Predicting the effects of extreme climatic events, such as heat waves, is a major challenge because they may affect both organisms and trophic interactions, leading to complex responses at the community level. In this study, we set up a simple three-level food chain composed of a sweet pepper plant, Capsicum annuum; an aphid, Myzus persicae; and a ladybeetle, Coleomegilla maculata, to explore the consequences of simulated heat waves on organism performance, trophic interactions, and population dynamics. We found that (1) heat waves do not affect plant biomass, significantly reduce the abundance and fecundity of aphids, and slightly affect ladybeetle developmental time and biomass, (2) heat waves decrease the impact of ladybeetles on aphid populations but do not modify the effect of aphids on plant biomass, and (3) food chains including predatory ladybeetles are more resistant to heat waves than a simple plant-aphid association, with aphid abundance being less influenced by heat waves in the presence of C. maculata. Our results suggest that more biodiverse ecosystems with predators exerting a strong biotic control are likely to be less influenced by abiotic factors and then more resistant to extreme climatic events than impoverished ecosystems lacking predators. Our study emphasizes the importance of assessing the effects of climatic change on each trophic level as well as on trophic interactions to further our understanding of the stability, resilience, and resistance of ecological communities under climatic forcing.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".