Beneficial insect assemblages in floral strips and comparison fields
Bibliographic record
Abstract
We installed thirteen floral strips next to rotationally managed agricultural fields in 2019 in Manitoba, Canada, and then monitored beneficial insects on field edges and within fields 1–3 years after establishment (2019–2022). Treatments included strip (crop fields with a floral strip located on one edge), control (crop fields with naturally occurring vegetation on the field edges), and unmanaged natural sites (no crop or enhancements). We measured ground beetle active density and Shannon diversity using pitfall traps. Bee and syrphid fly capture rates and Shannon diversity were measured with net transects, blue vane traps, and pan traps. We compared insect capture rates and diversities using Generalized linear mixed effect models and compared insect communities using permutational analysis of variance 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 in the control comparisons. Although 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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".