Microbiota in the Rhizosphere and Seed of Rice From China, With Reference to Their Transmission and Biogeography
Bibliographic record
Abstract
Seeds play key roles in the acquisition of plant pioneer microbiota, including the transmission of microbes from parent plants to offspring. However, seed microbial communities are mostly unknown, especially for their potential origin and factors influencing the structure and composition. In this study, samples of rice seed and rhizosphere were collected from Northeast and Central-south China in two harvest years and analyzed using a metabarcoding approach targeting 16S rRNA region. A higher level of vertical transmission (from parent seed microbiota to offspring) was revealed, as compared to the acquisition from the rhizosphere (25.5% vs. 10.7%). About 3.59% and 7.54% of the seed and rhizosphere OTUs were identified as their respective core microbiota, showing a smaller proposition of core taxa in seed. Among the seed core microbiota, members of bacterial genera like Pantoea, Pseudomonas and Xanthomonas have been reported as important rice pathogens. Both the seed and rhizosphere of rice showed distance-decay of similarity in microbial communities. Seed moisture and WMAT (winter mean annual temperature) had significant impacts on seed microbiota, while WMAT, AK (available kalium), AP (available phosphorus), Al, pH, and TN were the significant variables determining rhizosphere microbiota. By parsing microbial OTUs into function pathways, multiple seed and rhizosphere enriched pathways were characterized, which, to some extent, explained the potential adaptation of seed or rhizosphere microbiota to their living habitats. The results presented here elucidate the composition and possible sources of rice seed microbiota, which is crucial for the health and productivity management in sustainable agriculture.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".