Pollinators respond positively to urban green space enhancements using wild and ornamental flowers
Bibliographic record
Abstract
Abstract Pollinator diversity and abundance are under direct threat from human activities. Despite societal dependence on pollinators for crop production, humankind continues to drive pollinator declines through agricultural intensification and urbanisation. Urban environments can provide refuge to some pollinators. There is a need, however, to understand how pollinator communities can be supported in these areas while also considering human needs. Public green spaces provide a promising avenue to target plant–pollinator conservation measures in an urban setting due to their large area, high abundance and low plant–pollinator biodiversity. We used a paired design to compare the plant community, pollinator community and plant–pollinator interactions of (i) public urban green spaces enhanced with wildflower meadows and pollinator‐friendly ornamental plantings to (ii) control unenhanced urban green spaces. The planting types within enhanced sites were also separately compared. Our results show that not only was the diversity of the overall pollinator community higher in enhanced than control sites, but also the complexity of some wild Hymenoptera and the abundance of Diptera pollinator groups. The negative impact of urbanisation on some pollinator groups was reduced in enhanced sites compared with control sites. Planting both pollinator‐friendly ornamental plants and wildflower meadows significantly increased the visits from Diptera and the diversity in the plant–pollinator community compared with wildflower plantings alone. Our results suggest that enhancing public spaces for pollinators has positive effects on key groups and can help mitigate the impacts of urbanisation. Non‐native ornamental plants can also play a role in enhancing green spaces for pollinators while maintaining their recreational functions. This supports a mixed‐planting approach for improving public green spaces in urban areas for both people and nature.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".