Native and non-native plants attract diverse bees to urban gardens in California
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it