Native and non-native plants attract diverse bees to urban gardens in California
Bibliographic record
Abstract
Bees visit native and non-native plant species for pollen and nectar resources in urban, agricultural, and wildland environments. Results of an extensive survey of bee-flower collection records from 10 California cities from 2005-2011 were used to examine host-plant records of native and non-native ornamental plants to diverse native and non-native bee species; five cities were from northern California and five were from southern California. A total of 7,659 bees and their floral host plants were examined. Of these, 179 were Apis mellifera and 7,390 were non-Apis. Only four other non-native species (all in Megachilidae) were recorded in the survey, and together they accounted for 402 individuals. These bees have been databased in preparation for deposition in the University of California-Berkeley Essig Museum of Entomology. We identified 229 bee species and 42 genera visiting native and non-native plant types in urban areas. Of the 229 species, 71 bee species were collected from only native plants; 52 were collected from only non-native host plants; and 106 were collected from both types of plants. Native bee species were common on native plants and non-native plants, but there were substantially more non-native bee species visiting non-native plants compared to native plants. Flowering periods in months were similar for both types of plants, but non-natives tended to flower later in the year. We propose that using native and non-native plants improves habitat gardening by increasing opportunities for attracting a richer diversity of bee species and for longer periods. Knowing basic bee-flower relationships in an area is key to planning a bee habitat garden with a variety of plant types, regardless of their geographic origin.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".