VARIATION IN FLOWER MORPHOLOGY ENHANCES WILD BEE DIVERSITY IN URBAN ECOSYSTEMS
Bibliographic record
Abstract
Urbanization is a leading threat to biodiversity, but scientifically informed management of urban ecosystems can mitigate negative impacts. For wild bees, which are declining worldwide, careful consideration of flower choice in private and public green spaces could help preserve their diversity. While floral density and species richness are both linked to wild bee diversity, the mechanisms underlying these relationships remain poorly understood. Here, I tested two hypotheses relating the influence of floral trait composition to bee species richness, which I have termed the floral trait diversity and optimal floral trait hypotheses. To test my hypotheses, I assessed whether within-site variation in bee richness relates to variation in floral trait composition in urban green spaces across the city of Montreal, Canada. In addition to surveying flower species richness and floral density, I measured two floral traits which relate to pollinator feeding success, nectar sugar concentration and corolla length, as feeding is the main use of flowers by bees and therefore likely to impact their communities. After accounting for variation in floral density, I found that bee richness was positively related to community-wide variation in corolla length, supporting the floral trait diversity hypothesis. These findings suggest that although flower abundance and richness affect wild bee richness in urban ecosystems, the composition of flower morphologies can further shape pollinator communities. I conclude that an understanding of these mechanisms can positively impact conservation of urban wild bee communities.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".