Floral strips increase the abundance and spillover of beneficial insects in rotational crop fields
Bibliographic record
Abstract
Abstract Insects provide many ecological functions that support agricultural production, but beneficial insect populations are often lower in areas of intensive agriculture. Agronomic practices that support beneficial insect populations are important for sustainable crop production. We installed 13 floral strips next to agricultural fields in 2019 and then monitored beneficial insects on field edges and within fields 1–3 years after establishment. Treatments included strip, control (crop fields with naturally occurring vegetation) and unmanaged natural sites (no crop or enhancement). Ground beetle active density and Shannon diversity were greater within strips in 2020 but declined to similar levels as control sites in 2021. Though no effect of treatment was found, bees were more abundant in strips compared to other treatments. Both bee abundance and diversity at strip sites increased over time. Syrphid abundance and diversity were not affected by treatment. Spillover of bees into adjacent blooming and non‐blooming crops was twice as high in strip fields as the control comparisons, though no differences in spillover were found between treatments for syrphids or ground beetles. Floral strips increased the abundance of pollinators within adjacent crops, including blooming canola, which may lead to increased ecosystem services within crops. Our research supports the use of floral strips in rotational agriculture to manage local insect populations.
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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".