Variations in leaf phyllosphere microbial communities and development of tobacco brown spot before and after fungicide application
Bibliographic record
Abstract
In recent years, STROBY (50% Kresoxim-methyl) has been widely used to control tobacco brown spot in Guizhou Province, China. As a broad-spectrum fungicide, STROBY targets not only phytopathogens, but also affects many other microorganisms including those pathogenic, beneficial, or neutral to the plant hosts. To understand the effects of STROBY on the phyllosphere microbial communities of tobacco leaves during the development of tobacco brown spot, the fungal and bacterial communities of symptomatic and asymptomatic leaves at four time points, before spraying (August 29) and after spraying (September 3, 8, and 13), were investigated using the Illumina high-throughput sequencing. The results showed that STROBY had significant effects on the phyllosphere microbial communities of tobacco leaves. Microbial communities in asymptomatic leaves were more greatly affected than their counterparts in symptomatic leaves, and fungal communities were more sensitive than bacterial communities. Throughout the experiment, the most common genera in symptomatic leaves were Alternaria, Pseudomonas, Pantoea, and Sphingomonas, and in asymptomatic leaves, these were Golubevia and Pantoea. After spraying, the alpha diversity of fungal communities increased in symptomatic leaves and decreased in asymptomatic leaves, while the alpha diversity of bacteria increased in both types of leaves. Beta diversity showed that in asymptomatic leaves, the fungal communities in the first stage was significantly different from the remaining three stages. In contrast, the fungal communities in symptomatic leaves and the bacterial communities in all leaves did not fluctuate significantly during the four stages. Before spraying (August 29), the dominant functions of the fungal community were animal pathogen, endophyte, plant pathogen, and wood saprotroph. Whereas after spraying (September 3, 8, and 13), the proportion of the above fungal functions decreased and the unassigned functions increased, especially in asymptomatic leaves. This study describes the effects of STROBY application and tobacco brown spot presence in shaping the leaf phyllosphere microbial communities, and provides insights into the microbial community effects on tobacco leaves of a strobilurin fungicide.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".