How much do rare and crop-pollinating bees overlap in identity and flower preferences?
Bibliographic record
Abstract
1. The biodiversity-centered approach to conservation prioritizes rare species, whereas the ecosystem services approach prioritizes species that provide services to people. The two approaches align when rare species provide ecosystem services, or when both groups of species benefit from the same management action. We use data on bee pollinators and the plant species they forage on to determine if there are rare species among the most important crop pollinators, and the extent to which plant species selected to support crop pollinators would support rare species as well. 2. To assess the plant preferences of these two groups of bee species, we collected two datasets on plant-pollinator interactions, one experimental and one observational. The experiment consisted of monospecific plots of 17 plant species from which we collected bees over three years. The observational data consisted of bees collected from 66 species of plants growing in semi-natural meadows, also over three years. 3. Nineteen percent of the dominant crop pollinating species were regionally rare. Both rare species and crop-pollinating species had strong preferences for certain plants, and the preferences of rare and crop-pollinating bees were significantly but not strongly (r ≤ 0.54) correlated. Ten plant species were significantly preferred by both rare and crop-pollinating bees. 4. Synthesis and applications. We found several dominant crop pollinators that are rare at a regional scale, supporting the idea that rare species can be important providers of ecosystem services. The flower preferences of rare and crop-pollinating bees are significantly positively associated, suggesting that plants chosen to support crop pollinators will benefit rare species as well. We identify plant species that are preferred by regionally rare bees and by crop pollinators, including 10 plant species preferred by both types of bees, and recommend these for use in pollinator habitat plantings.28-Oct-2019
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".