Effects of Natural Habitat Loss and Edge Effects on Wild Bees and Pollination Services in Remnant Prairies
Bibliographic record
Abstract
Habitat loss and edge effects resulting from habitat fragmentation are key processes implicated in the decline of bee populations globally. Their effects on wild bees and their pollination services in natural ecosystems are poorly understood, particularly in North American prairies. Our objectives were to determine whether natural habitat loss and edge effects affect bee abundance and pollination services in the Northern Great Plains. We sampled bee abundance and pollination services along transects beginning at road or tree edges in grasslands located in Manitoba, Canada. We measured bee abundance using pan traps, and pollination services using seed-set of Brassica rapa (L.) (Brassicales: Brassicaceae) phytometers. We collected local-scale habitat data by measuring occurrence of flowering species, vegetation type, and vegetation structure, and we measured habitat amount at 1-km radii using GIS analysis of landscape cover. Increasing amounts of habitat loss resulted in declines in bee abundance, and sometimes in pollination services. Results varied with bee life-history: proximity to road edges negatively affected social bees, and litter depth had negative effects on below- ground-nesting bees. Surprisingly, few effects on bees led to corresponding impacts on pollination services. This suggests that conservation of intact natural habitat across the northern Great Plains is important for maintaining resilient and diverse bee communities, but that efforts to conserve bee populations cannot be assumed to also maintain all associated pollination services.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".