The effects of drought and herbivory on plant–herbivore interactions across 16 soybean genotypes in a field experiment
Bibliographic record
Abstract
Abstract As the Earth's climate continues to change, drought and insect population outbreaks are predicted to increase in many parts of the world. It is therefore important to understand how changes in such abiotic and biotic stressors might impact agroecosystems. The plant stress hypothesis predicts that, owing to physiological and biochemical changes, plants experiencing drought will be more susceptible to insect herbivory, which could have synergistic negative effects on plant performance. By contrast, the plant vigor hypothesis predicts that insects will preferentially feed on fast‐growing vigorous plants. These hypotheses were tested in a field experiment using 16 soybean ( Glycine max ( L .) M err.) genotypes to determine: (i) the combined effects of drought and herbivory on plant performance; (ii) the impact of drought on soybean resistance to herbivores; and (iii) how genetically variable phenotypic traits in soybean correlate with these responses. It was found that drought had a greater effect on soybean performance than herbivory, and drought and herbivory did not interact to impact on any measure of plant performance. Drought caused decreased insect herbivory on average, suggesting that the plant vigor hypothesis is consistent with the effects of drought stress on soybean resistance to leaf‐chewing insect herbivores. This conclusion is further supported by genotypic correlations which show that plant growth rate is positively correlated with the amount of herbivory plants received. These results suggest that, although the effects of climate‐associated changes in drought and herbivory will have negative effects on soybean, these potential effects are quantifiable with simple experiments and can be mitigated through continued breeding of varieties that are tolerant and resistant to these abiotic and biotic stressors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".