Annual Crops Contribute More Predators than Perennial Habitats during an Aphid Outbreak
Bibliographic record
Abstract
Crops and semi-natural habitats provide predator populations with varying floral and prey resources, but their individual role on predator movement has seldom been studied. Here, we tease apart the role of adjacent habitats, predator abundance in the adjacent habitat, and soybean aphid (Aphis glycines Matsumura) abundance in soybean (Glycine max (L.) Merr.) on predator movement into soybean. We studied 12 soybean fields adjacent to alfalfa (Medicago sativa L.), canola (Brassica napus L.), spring wheat (Triticum aestivum L.), or woody vegetation, during a soybean aphid outbreak. Bidirectional Malaise traps and sticky traps were used to quantify predator movement between and abundance within soybean and adjacent habitats, respectively. Field plant counts were conducted to quantify aphid abundance in soybean. Coccinellidae and Syrphidae were the two most abundant families collected. Coccinellids and Eupeodes americanus (Wiedemann) (Diptera: Syrphidae) had net movement in soybean and their movement increased with aphid abundance. Movement of E. americanus was highest from wheat, coccinellid abundance was higher in wheat than woody vegetation, Toxomerus marginatus (Say) (Diptera: Syrphidae) abundance was highest in canola, and all other predators were more abundant in canola than woody vegetation. In general, our study suggests that annual crops have and provide more predators to soybean during aphid outbreaks than perennial habitats.
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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 | 0.000 | 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".