Arthropod arbiters: natural enemy communities mediate the effects of landscape and local-scale complexity on Lygus-induced crop loss in organic strawberries
Bibliographic record
Abstract
Sustainable pest management requires growers and regional land managers to consider the relationships among pest management practices, pest and natural enemy communities, crop loss, and multi-scalar habitat complexity. However, the causal links among these variables, in particular potential interactions between landscape and local-scale habitat complexity, remain underexplored. In the context of organically managed strawberry crops in California’s Central Coast, we tested the independent effects of landscape and local habitat complexity gradients on arthropod communities and crop loss using a piecewise structural equation model (PSEM). We found that landscape-scale woody habitat proportion indirectly decreased crop loss through its positive effect on natural enemy abundance, while grassland proportion had the opposite effect due to its association with an important strawberry pest (Lygus spp.). We detected a pattern suggesting that on-farm diversification practices are most effective at reducing crop loss at an intermediate level (26%) of woody habitat proportion. Both organic-compliant insecticide application and tractor vacuuming negatively impacted natural enemies, and therefore had qualified effects on crop loss. Our study shows the key roles of native woodlands and natural enemy communities in reducing crop loss and highlights the importance of managing habitat complexity at both landscape and local scales.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".