Variation in flower morphology associated with higher bee diversity in urban green spaces
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 public and private 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 are not fully understood. Here, we tested two hypotheses relating the influence of floral trait composition to bee species richness, which we have termed the within-trait diversity and optimal floral trait hypotheses. Specifically, we assessed whether variation in bee richness relates to variation in the weighted variance (trait diversity) and mean (optimal trait) of floral traits observed in urban green spaces across the city of Montreal, Canada. Our analyses focused on two floral traits relating to pollinator feeding success: nectar sugar concentration and corolla length. After accounting for variation in floral density among sites, bee richness was positively related to community-weighted variance in corolla length, supporting the within-trait diversity hypothesis. These findings suggest that management practices that increase the diversity of flower morphologies in urban green spaces can promote the persistence of wild bee communities in cities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".