Plant diversity and origin do not predict abundance and diversity of syrphid flies (Diptera: Syrphidae) in small urban gardens
Bibliographic record
Abstract
Abstract Gardens have emerged as a key habitat resource for pollinators in cities, but more research is needed to determine the optimal garden characteristics for maximising native pollinator diversity. Syrphid flies (Diptera: Syrphidae) are abundant generalist fly pollinators that have received less study than other pollinators in urban gardens. In this study, we investigated whether flowering plant diversity and the presence of native plants were related to syrphid abundance and diversity in urban street gardens. Over a two-month period, we sampled 12 small public gardens in a residential urban area (Vancouver, British Columbia, Canada) to explore correlations between plant and syrphid assemblages. Gardens reflected the relative scarcity of native plants in our study system, such that gardens with native flowers present ranged from 10 to 60% cover. Although syrphid abundance and richness varied among gardens, neither floral richness nor the presence of native flowers was correlated with syrphid abundance or diversity. Beyond plant diversity and origin, other characteristics may be more important to syrphid visitation at the garden scale. A better understanding of the role of garden characteristics among the complex factors shaping urban syrphid assemblages will offer valuable insights for the improvement of pollinator conservation strategies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".