Co-occurrence patterns between bacterial and fungal communities in response to a vegetation gradient in a freshwater wetland
Bibliographic record
Abstract
Vegetation dynamics are known to influence belowground microbial community diversity and ecosystem processes in wetlands. However, the knowledge on microbe-microbe interactions in response to vegetation changes is scarce. In this study, we investigated how bacterial and fungal community composition, as well as bacterial-fungal community interactions, altered along a vegetation gradient in the Poyang Lake wetland. Surface soil and sediment samples were collected from three vegetation zones: dense, sparse, and naked. Vegetation zones differed in terms of dominant plant species, plant diversity, and vegetation coverage. Using Illumina MiSeq sequencing and network analysis of bacteria 16S rRNA and fungal ITS genes, we found that both bacterial and fungal community profiles varied according to vegetation conditions; in particular, the dense vegetation zone facilitated higher microbial abundance and a greater fungi to bacteria ratio. Co-occurrence analysis revealed that fungi-bacteria interactions were strong on vegetated zones, especially in the dense vegetation zone. However, a weak fungi-bacteria association was observed in the naked zone. Our results indicated that aboveground vegetation may act as a hotspot for organic matter accumulation, microbial growth, and microbe-microbe interactions, whereas fungi and bacteria prefer to distribute into niches based on their own nutritional preferences and functional specificity in bare ground.
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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 | 0.001 |
| 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 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".