Generalist predators function as pest specialists: Examining diet composition of spiders and ladybeetles across rice crop stages
Bibliographic record
Abstract
Abstract Biocontrol, the use of natural enemies to manage pests, has a long history in agriculture. It has gained renewed interest because of its importance in sustainable agriculture. To solve a long‐standing puzzle in biocontrol—how well the ubiquitous generalist arthropod predators (GAPs) function as biocontrol agents—this study aimed to (1) quantify the diet composition of GAPs (spiders and ladybeetles) at different crop stages using stable isotope analysis, (2) examine the consistency of GAPs in pest consumption over years and (3) investigate how abiotic and biotic factors (farm type, crop stage, surrounding vegetation and relative prey abundance) affect pest consumption by GAPs. Specifically, we sampled arthropod prey and GAPs in seven pairs of sub‐tropical organic and conventional rice farms over crop stages (seedling, tillering, flowering and ripening) in three consecutive years. Among our sweep‐net samples, 352 arthropod predator and 828 prey isotope samples were analysed to infer predator–prey interactions. Our results show the following: (a) The proportion of rice pests in GAPs' diets in both organic and conventional rice farms increased over the crop season, from 21% to 47% at the tillering stage to 80%–97% at the ripening stage, across the three study years. The high percentage in pest consumption at late crop stages (flowering and ripening) suggests that GAPs can function as specialists in pest management during the critical period of crop production. Regarding individual predator groups, spiders and ladybeetles exhibited distinct dietary patterns over crop stages. (b) The high pest consumption by GAPs at late crop stages was similar across years despite variable climatic conditions and prey availability, suggesting a consistency in GAP feeding habits and biocontrol value. (c) The proportion of rice pests in GAPs' diets varied with farm type and crop stage (e.g. higher in conventional farms and during flowering/ripening stages). Synthesis and applications. By quantifying the diet composition of GAPs over crop stages, farm types and years, this study reveals that generalist predators have potential to produce a stable, predictable top‐down effect on pests in rice agro‐ecosystems. Therefore, promoting the field densities of ubiquitous generalist predators will likely enhance pest management and support sustainable agriculture.
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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 | 0.001 | 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".