Supporting Bees in Cities: How Bees Are Influenced by Local and Landscape Features
Bibliographic record
Abstract
Urbanization is a major anthropogenic driver of decline for ecologically and economically important taxa including bees. Despite their generally negative impact on pollinators, cities can display a surprising degree of biodiversity compared to other landscapes. The pollinating communities found within these environments, however, tend to be filtered by interacting local and landscape features that comprise the urban matrix. Landscape and local features exert variable influence on pollinators within and across taxa, which ultimately affects community composition in such a way that contributes to functional trait homogenization and reduced phylogenetic diversity. Although previous results are not easily generalizable, bees and pollinators displaying functional trait characteristics such as polylectic diet, cavity-nesting behavior, and later emergence appear most abundant across different examined cities. To preserve particularly vulnerable species, most notably specialists that have become underrepresented within city communities, green spaces like parks and urban gardens have been examined as potential refuges. Such spaces are scattered across the urban matrix and vary in pollinator resource availability. Therefore, ensuring such spaces are optimized for pollinators is imperative. This review examines how urban features affect pollinators in addition to ways these green spaces can be manipulated to promote greater pollinator abundance and 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".