Responses of Insect Pests and Plant Diseases to Changing and Variable Climate: A Review
Bibliographic record
Abstract
Natural and anthropogenic factors have resulted in altered environmental conditions that influence changes in abundance and diversity of insect pests. Global climate change projections focus on crop yields and adaptation strategies to declining yields and ignore the likely impact of a changing climate on insect pests and plant diseases. In this research paper, we review the effects of climate variables namely temperature, carbon dioxide (CO2), precipitation and extreme weather events on insect pests and plant diseases incidence. Elevated temperatures, CO2 and extreme weather events have been shown to alter the distribution, reproductive potential, the incidence and abundance of plant insects and diseases in temperate regions because of the dependence of insects and diseases on environmental conditions. There is limited information on the influence of temperature and carbon dioxide as well as their interaction on the incidence and severity of insect pests, bacterial and viral diseases in the tropical regions. Information on the influence of altered precipitation patterns is also limited but could be of importance in insect distribution studies in a changing climate. Some tropical insects pests are most likely to suffer from extreme heat, resulting in death and hence pest extinction. Future research should focus on the interaction of elevated temperature and CO2, determine the influence of supra optimal summer temperatures, temperature variability, precipitation variability and the corresponding viral and bacterial diseases.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".