Pollinator visits increase with bloom amount but decline with building height on extensive green roofs
Bibliographic record
Abstract
Abstract Green roofs provide foraging and nesting resources for pollinators that would otherwise be absent. However, green roofs are isolated from ground level, limiting habitat to only species that can reach them. In Eastern North America, green roof design often prioritises water conservation and plant survival, and so species in the genera Sedum and Phedimus (hereafter, stonecrops) that are hardy and drought tolerant are mainly planted. The purpose of this study was to investigate how building height and bloom amount shape flower‐visiting insect communities on extensive green roofs (EGRs). Bees, wasps, and flies (hereafter, pollinators) were surveyed on five plant species (four non‐native stonecrops: Sedum acre , Sedum album , Phedimus kamtschaticus , Phedimus spurius , and one native herbaceous plant: Rudbeckia hirta ) from six EGRs and two replicated ground‐level control sites in 2019. We identified 26 pollinator species and found that stonecrops and Rudbeckia showed distinct blooming periods, with the stonecrops flowering from June to August and Rudbeckia from August to September. Percent flowering stonecrops during the early bloom was significantly positively correlated with bee abundance and species richness. Pollinator communities determined from distinct stonecrop species were compositionally more alike to one another than R. hirta . The inclusion of R. hirta lengthened the bloom period of stonecrop ‐ dominated EGRs and attracted five additional bee species. We further determined that pollinator abundance and species richness were negatively correlated with building height. Despite its limited scope, our data suggest that pollinator habitat design on EGRs should prioritise low‐rise buildings and flowering species with abundant blooms that occur at different times than stonecrops, ensuring complementary flowering periods.
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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".