Herbivorous Insects in Agroecosystems: Evolutionary Adaptations and Species Dynamics
Bibliographic record
Abstract
This study analyzed the biological characteristics, ecological roles, evolutionary adaptation mechanisms and population dynamics of herbivorous insects in agricultural ecosystems, discussed the classification and ecological habits of herbivorous insects, their impact on agricultural production and ecological network functions, and the concept of adaptive evolution unique to agricultural environments, focusing on the evolutionary adaptation mechanisms of herbivorous insects in terms of nutrient utilization, behavioral sensory perception and pesticide resistance. At the same time, the driving factors of their population dynamics were explored, including environmental factors, agricultural management measures and the impact of climate change. Through typical cases such as cotton bollworm, whitefly, Spodoptera litura , and rice planthopper, the phenomenon of multi-host adaptation, resistance evolution, population replacement and global expansion of herbivorous insects was analyzed. This study also looks forward to future research directions, such as multi-omics integration to reveal adaptation mechanisms, precision agriculture and population prediction models, biological regulation and ecological agriculture strategies, and adaptive evolution risk assessment under the background of climate change, in order to guide sustainable integrated pest management and provide reference for the stability of agricultural ecosystems and food security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".