California Native Perennials Attract Greater Native Pollinator Abundance and Diversity Than Nonnative, Commercially Available Ornamentals in Southern California
Bibliographic record
Abstract
While many factors have been implicated in global pollinator decline, habitat loss is a key driver of wild pollinator decline in both abundance and species richness. An increase in and diversification of pollinator habitat, even in urban settings, can assist in the conservation of pollinator populations. In Southern California, a highly fragmented and urbanized landscape with a rich yet threatened native pollinator fauna, the availability of food resources for native pollinators hinges largely upon the selection of ornamental plants grown in the urban landscape. To examine the pollinator attractiveness of ornamental plants in a Southern California context, we installed an experimental garden with common California native and nonnative ornamental perennials and observed floral visitation and visitor community composition for 3 yr. Our study demonstrates that while native pollinators visited common ornamental perennials native to California at a higher rate than they visited nonnative ornamentals, introduced honey bees showed no significant preference for either native or nonnative species. Native plants also received a greater diversity of visitor taxa, including a richer suite of native bees. Plant species differed dramatically in attractiveness, by as much as a factor of 12, even within the native status group. Our results suggest that including a data-driven selection of both native and non-native ornamental perennials in the urban landscape can diversify the assemblage of native pollinators, provide critical floral resources throughout the year, and reduce the impact of honey bee landscape foraging dominance by providing plants highly attractive to native pollinators and less so to honey bees.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".