Native bee genus diversity within bee-friendly urban gardens varies little along an urbanization gradient
Bibliographic record
Abstract
Urban bee populations are threatened by ongoing habitat loss and fragmentation. Bee-friendly gardens with abundant forage and nesting resources may help offset these pressures, but attributes of the broader urban landscape could also play an important role. We explored bee diversity within 32 bee-friendly gardens distributed throughout the city of Kelowna, British Columbia. Our objectives were to (i) estimate bee genus abundance, richness, and composition within the gardens, and (ii) investigate how these facets of diversity varied with plant diversity, garden size, and degree of urbanization within 300m of the garden. In total, over 10 sampling days per garden, we observed 19 genera—about half of those known from the region—primarily within the Halictidae family (66.4% of total abundance). Neither bee genus richness nor total bee abundance was associated with any of the explanatory variables, whereas a minor (though statistically significant) percentage of the among-site variance in genus composition was uniquely accounted for by the urbanization gradient (6.8% with rare genera excluded, 5.8% when included). Our study is the first to evaluate urban bee diversity within the Okanagan diversity hotspot and our findings add to previous studies elsewhere demonstrating inconsistent effects of garden and landscape-scale characteristics on bee diversity.
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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 | 0.001 |
| 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".