Tripartite interactions of an endophytic entomopathogenic fungus, Asian corn borer, and host maize under elevated <scp>carbon dioxide</scp>
Bibliographic record
Abstract
Abstract BACKGROUND Biological control of insect pests is encountering an unprecedented challenge in agricultural systems due to the ongoing rise in carbon dioxide (CO 2 ) level. The use of entomopathogenic fungi (EPF) in these systems is gaining increased attention, and EPF as crop endophytes hold the potential for combining insect pest control and yield enhancement of crops, but the effects of increased CO 2 concentration on this interaction are poorly understood. Here, the introduction of endophytic EPF was explored as an alternative sustainable management strategy benefiting crops under elevated CO 2 , using maize ( Zea mays ), Asian corn borer ( Ostrinia furnacalis ), and EPF ( Beauveria bassiana ) to test changes in damage to maize plants from O. furnacalis , and the nutritional status (content of carbon, nitrogen, phosphorus, potassium), biomass, and yield of maize. RESULTS The results showed that endophytic B. bassiana could alleviate the damage caused by O. furnacalis larvae for maize plants under ambient CO 2 concentration, and this effect was enhanced under higher CO 2 concentration. Inoculation with B. bassiana effectively counteracted the adverse impact of elevated CO 2 on maize plants by preserving the nitrogen content at its baseline level (comparable with ambient CO 2 conditions without B. bassiana ). Both simultaneous effects could explain the improvement of biomass and yield of maize under B. bassiana inoculation and elevated CO 2 . CONCLUSION This finding provides key information about the multifaceted benefits of B. bassiana as a maize endophyte. Our results highlight the promising potential of incorporating EPF as endophytes into integrated pest management strategies, particularly under elevated CO 2 concentrations. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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".