Forest cover enhances natural enemy diversity and biological control services in Brazilian sun coffee plantations
Bibliographic record
Abstract
Abstract Landscape structure and crop management directly affect insect communities, which can influence agriculturally relevant ecosystem services and disservices. However, little is known about the effect of landscape structure and local factors on pests, natural enemies, and biological control services in the Neotropics. We investigated how environmental conditions at local and landscape levels affect Leucoptera coffeella (insect pest), social wasps (natural enemies), and the provision of biological control services in 16 Brazilian coffee plantations under different crop management and landscape contexts. We considered microclimatic conditions, coffee plantation size, and management intensity at the local level; and forest cover, landscape diversity, and edge density at the landscape level. Pest population, wasp communities, and biocontrol services were monitored in wet and dry seasons when L . coffeella outbreaks occur. We found that the amount of forest in the surrounding landscape was more important for explaining patterns than the local environment, landscape diversity, or landscape configuration. In both seasons, L . coffeella was negatively affected by forest cover, whereas biological control and richness and abundance of social wasps increased with increasing forest cover at multiple spatial scales. Moreover, biological control was positively correlated with wasp abundance during pest outbreaks, suggesting that social wasps are important natural enemies and provide pest control services within coffee plantations. We provide the first empirical evidence that forest cover is important for the maintenance of social wasp diversity and associated pest control services in a Brazilian coffee-producing region.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".