Regional plant abundance explains patterns of host use by pollen‐specialist bees in eastern <scp>N</scp>orth <scp>A</scp>merica
Bibliographic record
Abstract
Specialist insect herbivores make up a substantial fraction of Earth's biodiversity; however, they exploit a minority of plant lineages. For instance, in the eastern United States and Canada, ~25% of bee species are pollen specialists, but they are hosted by a small fraction of the native, animal-pollinated angiosperms in the region: Only 6% of plant genera and 3% of families support pollen-specialist bees. It is unclear why some plant lineages host specialist bees while others do not. We know that at least some specialist bees use plant taxa that are avoided by generalists, suggesting that specialist bees favor plants with low-quality pollen, potentially as a strategy to escape competition or obtain protection from natural enemies. There is also evidence that specialist bees prefer superabundant host plants. Here we investigate whether pollen quality and plant abundance predict patterns of host use by specialist bees in eastern North America. Through field observations, we find that plants hosting specialist bees are frequent sources of pollen for generalists, suggesting that their pollen is not generally avoided by bees due to poor pollen quality. In addition, our analysis of a large citizen-science data set shows that regional abundance strongly predicts which plant genera in the eastern United States host pollen-specialist bees. Our results show that bees specialize on regionally abundant-but not necessarily low-quality-plant lineages. These plant lineages may provide more opportunities for the evolution of specialists and lower likelihood of specialist extinction.
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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) | 0.003 | 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".