Elevated CO<sub>2</sub> concentration improves the performance of an agricultural pest: a worrisome climate crisis scenario
Bibliographic record
Abstract
Abstract Carbon dioxide (CO2) emissions are central to the climate crisis and their consequences indiscriminately affect natural and anthropogenic ecosystems. Among ecological interactions, those between plants and insects are among the most impacted by the elevation in CO2 concentration (eCO2). We selected a plant and an herbivore species of worldwide relevance and tested the hypothesis that eCO2 affects leaf quality and defences of sunflower, Helianthus annuus L. (Asteraceae), and negatively impacts the larval preference and performance of the important pest Helicoverpa armigera Hübner (Lepidoptera: Noctuidae). Plants and insects developed inside open‐top chambers under ambient CO2 (ca. 400 ppm) and eCO2 (ca. 800 ppm). Sunflowers under eCO2 grew more (e.g., increased height and had more leaves) but were of lower nutritional quality at an early developmental stage (e.g., lower nitrogen content, greater leaf thickness, and higher flavonoids content). Despite showing no preference for either treatment, H. armigera larvae performed better when fed with leaves from eCO2 plants. We argue this was observed because larvae under eCO2 sustained a greater leaf consumption, even when sunflower leaf quality became similar between treatments (by the 11th week after germination). Besides, they overcompensated a more deficient diet during early developmental stages and presented a higher growth rate; ca. 2.5× more individuals reached the pupal stage, and 4× more individuals became adults. The improvement in H. armigera larval performance under eCO2 indicates a worrisome scenario in which a species that already exerts a significant impact on ecosystems would increase its consumption, develop faster, and support a larger population size.
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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".