Non-target effects of agri-environmental schemes on solitary bees and fungi in the United Kingdom
Bibliographic record
Abstract
) are well-studied, the effects on non-target bee species like solitary bees, in the same environment, are generally lacking. One goal of AES is to alter floral provision and taxonomic composition of plant communities to provide better forage for pollinators, however, this may potentially impact other ecological communities such as fungal diversity associated with plant-bee communities. Fungi are integral in these bee communities as they can impact bee species both beneficially and detrimentally. We test the hypothesis that alteration of the environment through provision of novel plant communities has non-target effects on the fungi associated with solitary bee communities. We analyse fungal diversity and ecological networks formed between fungi and solitary bees present on 15 agricultural farms in the UK using samples from brood cells. The farms were allocated to two categories, low and high management, which differ in the number of agri-environmental measures implemented. Using internal transcribed spacer metabarcoding, we identified 456 fungal taxa that interact with solitary bees. Of these, 202 (approximately 44%) could be assigned to functional groups, the majority being pathotrophic and saprotrophic species. A large proportion was Ascosphaeraceae, a family of bee-specialist fungi. We considered the connectance, nestedness, modularity, nestedness overlap and decreasing fill, linkage density and fungal generality of the farms' bee-fungi ecological networks. We found no difference in the structure of bee-fungi ecological networks between low and high management farms, suggesting floral provision by AES has no significant impact on interactions between these two taxonomic groups. However, bee emergence was lower on the low management farms compared to high management, suggesting some limited non-target effects of AES. This study characterizes the fungal community associated with solitary bees and provides evidence that floral provision through AES does not impact fungal interactions.
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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 | 0.002 |
| 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) | 0.004 | 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".