Investigating bee dietary preferences along a gradient of floral resources: how does resource use align with resource availability?
Bibliographic record
Abstract
Bee dietary preferences, or the floral resources that they consistently collect, likely impact where a species can persist. For this reason it is likely that bee dietary preferences are dependent upon the composition of the plant community. In this study, we evaluated floral visits and pollen loads of the mining bee, Andrena angustitarsata Viereck, across a 630 km north-south range to understand dietary preferences along a floral resource gradient. Previous research, in a more geographically limited area, suggested this species was an eclectic oligolege on predominantly Apiaceae and in part Rosaceae. In the present study we found the species predominately visited and collected pollen from Apiaceae and Rosaceae, but visited 12 flower families and collected pollen from 32, distinguishing them as generalist foragers. The frequency of Apiaceae pollen on the bees and the species-level specialization index (a measure of visit specialization) were higher in regions with higher Apiaceae abundance. In addition Apiaceae and Rosaceae were the only plant families significantly preferred for pollen collection, regardless of floral abundance. We conclude that across our study region A. angustitarsata has a generalist dietary breadth, but also has dietary preference for Apiaceae and Rosaceae. Our study indicates that while bees may overall make generalist foraging decisions they may still prefer and likely benefit from selecting fewer flower taxa.
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 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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".