Research progresses of plant-herbivore interactions
Bibliographic record
Abstract
Plant-herbivore interaction is one of the most common and important interspecific relationships in nature, which is the core and foundation of the food web theory.In this paper, we review the effects of herbivores on the characteristics of plant individuals, populations and communities, as well as the defense strategies and mechanisms of plants against herbivores at the levels of individuals, populations and communities.Herbivory can significantly change the growth, reproduction and survival rates of plant individuals and populations, which can in turn affect the composition and diversity of plant communities.In order to defend against herbivory, plants have evolved a series of defense mechanisms at the individual, population and community levels.At the individual and population levels, plants avoid herbivory mainly by chemical and physical defense.At the community level, however, plant defenses are achieved mainly by their influences on the behaviors of herbivores.This paper then introduces and compares important hypotheses and theories in related fields.Finally, we point out major existing research issues and identify possible future research directions.Given that natural systems are experiencing strong disturbances from human activities and climate changes, exploring how these disturbances affect plant-animal interactions, and how these changes in plant-animal interactions feedback on the structure, function and stability of the ecosystems, will not only have important theoretical significance, but also help us to formulate successful ecosystem management policy in the future.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".