Native bee communities vary across three prairie ecoregions due to land use, climate, sampling method and bee life history traits
Bibliographic record
Abstract
Abstract Recent evidence indicates that many native bee species are in decline due to the cumulative effects of multiple human‐induced stressors such as habitat loss, pesticide exposure, pathogens, and climate change. These declines have raised interest in the status of native bees and in developing tools that support management of bee communities and the ecosystem services they deliver. Native bees were surveyed using pan traps and netting over 2 years at 68 locations in croplands and rangelands across three ecological regions of Alberta's prairies – the Grassland, Parkland, and Boreal Natural Regions – to evaluate patterns in bee communities in response to disturbance and ecological gradients. Bee community composition was different across land use and ecoregions. While several cavity‐nesting species had a strong association with rangelands, cavity‐nesting bees tended to be less common in croplands and may be more sensitive to loss of rangeland habitat. Response patterns in overall bee abundance and richness were driven by interactions between region and land use, highlighting the need for regional studies to understand how bee communities respond to these factors. This survey is one of the first to sample the response of bee communities to landscape disturbance across a broad spatial area of the Canadian prairies. Large‐scale compositional studies are essential for understanding the status of native bee communities, and for monitoring long‐term trends over time. We recommend subsequent coordinated surveys using standardised methods across broad spatial scales.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".