Does composition of tropical agricultural landscape affect parasitoid diversity and their host–parasitoid interactions?
Bibliographic record
Abstract
Abstract The expansion of agricultural fields is the main cause of landscape simplification and changes in the composition and configuration of landscapes. These landscape changes influence pests and their natural enemies, and may influence their interaction and the biological control services to which they contribute. However, the effects of landscape composition can vary between region and insect species. The present study was conducted on cucumber plants in 16 different sites of varying landscape composition in Bogor and its surrounding area, West Java, Indonesia. Sampling of insects was performed on lepidopteran pests and their parasitoids. The results obtained showed that landscape composition (i.e. patch number of cropland) had a positive impact on the abundance of parasitoids and their host (lepidopteran pests). Yet the proportion of natural habitat did not influence parasitoid abundance and their host–parasitoid interaction. The presence of Apanteles taragamae (i.e. the most abundant parasitoid) in the agricultural landscape was affected by the abundance of its hyperparasitoid and its host ( Diaphania indica ) and not by landscape composition. The findings of the present study suggest that the higher proportion of patchy cropland in tropical agricultural landscapes, as well as agricultural practices, can maintain parasitoid abundance and functional diversity and, finally, an enhanced parasitism rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".