Rising atmospheric CO<sub>2</sub>is reducing the protein concentration of a floral pollen source essential for North American bees
Bibliographic record
Abstract
At present, there is substantive evidence that the nutritional content of agriculturally important food crops will decrease in response to rising levels of atmospheric carbon dioxide, Ca However, whether Ca-induced declines in nutritional quality are also occurring for pollinator food sources is unknown. Flowering late in the season, goldenrod (Solidago spp.) pollen is a widely available autumnal food source commonly acknowledged by apiarists to be essential to native bee (e.g. Bombus spp.) and honeybee (Apis mellifera) health and winter survival. Using floral collections obtained from the Smithsonian Natural History Museum, we quantified Ca-induced temporal changes in pollen protein concentration of Canada goldenrod (Solidago canadensis), the most wide spread Solidago taxon, from hundreds of samples collected throughout the USA and southern Canada over the period 1842-2014 (i.e. a Ca from approx. 280 to 398 ppm). In addition, we conducted a 2 year in situtrial of S. Canadensis populations grown along a continuous Ca gradient from approximately 280 to 500 ppm. The historical data indicated a strong significant correlation between recent increases in Ca and reductions in pollen protein concentration (r(2)= 0.81). Experimental data confirmed this decrease in pollen protein concentration, and indicated that it would be ongoing as Ca continues to rise in the near term, i.e. to 500 ppm (r(2)= 0.88). While additional data are needed to quantify the subsequent effects of reduced protein concentration for Canada goldenrod on bee health and population stability, these results are the first to indicate that increasing Ca can reduce protein content of a floral pollen source widely used by North American bees.
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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 | 0.000 |
| 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 | 0.001 | 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".