Local plant richness predicts bee abundance and diversity in a study of urban residential yards
Bibliographic record
Abstract
Abstract Understanding the drivers of biodiversity in cities is a central goal of urban ecology. There is currently intense scientific and public interest in the factors that influence pollinator diversity in cities and their surroundings. Existing studies point to a variety of landscape and local factors as potentially important, including urbanization (often defined as impervious surface cover in the surrounding lands), tree canopy cover and the diversity and abundance of locally flowering plants. However, few studies have sought to weigh the relative importance of these predictors of bee community metrics. Using a set of 27 residential yards chosen to represent a gradient of both urbanization and tree canopy cover at a landscape scale, we used pan trapping and netting to assess the abundance and diversity of local bee communities across the City of Ottawa, Ontario, Canada. Surprisingly, the landscape factors (urbanization and tree cover) described only a tiny fraction (< 1%) of the total variance in bee abundance and diversity across sites. This was true regardless of the scale of analysis at which the landscape factors were measured. Instead, a yard's floral richness, and, to a somewhat lesser extent, its floral abundance, emerged as the most important predictors of a yard's bee community abundance and diversity. Our study offers an important counterpoint to a growing body of work emphasizing the impacts of landscape factors on bee communities. Instead, our research suggests that improving bee floral resources by increasing the plant species richness and abundance locally is a powerful tool to support bee conservation, regardless of the level of urbanization or tree cover in the surrounding landscape. Our work highlights that the practice of promoting ‘bee-friendly’ plantings in private yards, currently being undertaken by a number of non-profits around the world, can play an important role in restoring and maintaining urban pollinator 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".