Local and Landscape-Scale Features Influence Bumble Bee (Hymenoptera: Apidae) Bycatch in Bertha Armyworm Mamestra configurata (Lepidoptera: Noctuidae) Pheromone-Baited Monitoring Traps
Bibliographic record
Abstract
The bertha armyworm (BAW) Mamestra configurata Walker is a significant pest of canola Brassica napus L. (Brassicales: Brassicaceae) in western Canada. Its activity is monitored through a large network of pheromone-baited monitoring traps as a part of the Prairie Pest Monitoring Network across the Canadian Prairies. The unintentional bycatch of bee pollinators in pheromone-baited traps targeting moth pests occurs in many agroecosystems and may have repercussions for biodiversity and pollination services of wild plants and managed crops. We conducted field experiments to determine the abundance and diversity of bees attracted to green-colored BAW pheromone-baited traps across the canola growing regions of Alberta, Canada. A higher species diversity and more bumble bees were captured in BAW pheromone-baited than in unbaited control traps. Bombus rufocinctus Cresson (Hymenoptera: Apidae) was the most commonly captured species. Few other wild bees or honey bees Apis mellifera L. (Hymenoptera: Apidae) were captured during this study. Additionally, we evaluated the influence of local and landscape-level habitat features on bee bycatch. Local flowering plant abundance improved overall model fit but did not directly impact bee bycatch. The proportion of natural and seminatural habitat, and especially forested area, in the area surrounding monitoring traps affected bee bycatch. Both local and landscape-scale factors were important in this study and often have combined effects on bee communities. This study provides recommendations to reduce the bycatch of beneficial bee pollinators in a large-scale pheromone-baited monitoring network.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".