Effects of Different Vegetation Restoration Types on Soil Fungal Community Composition and Functional Groups
Bibliographic record
Abstract
Abstract Purpose This study investigates the structure and functional groups of soil fungal communities in major vegetation types in subtropical China. The main objective is to understand the responses of these communities to different vegetation types, and the influence of soil properties, such as soil organic carbon (SOC), pH, soil ammonium nitrogen (AN), available potassium (AK), and nitrate nitrogen (NN), on the structure and functional groups of the soil fungal communities. Methods We explored the impact of different vegetation types on fungal diversity in various plantations, including bare land, Liriodendron chinense (Hu) T.C. Chen, Cunninghamia lanceolata (Lamb.) Hook, Phyllostachys pubescens Mazel ex H.de Lehaie (moso bamboo), and mixed fores. We also analyzed variations in soil properties across different soil depths. Results The results showed that the soil available potassium (AK) and soil nitrate nitrogen (NN) in mixed forests were significantly lower than those in bare land by 47% and 57%, respectively. The Soil organic carbon (SOC), NN, and pH decreased significantly by 49%, 45%, and 8%, respectively with increasing the soil depth. The diversity of soil fungal communities in mixed forest and mandarin forest was 20% higher than that in bare land. The relative abundance of Ectomycorrhizal fungi was highest in bamboo forests, while the relative abundance of Arbuscular Mycorrhizal fungi and plant pathogens increased with increasing soil depth, by 12% and 7%, respectively. Conclusions Our findings indicate that vegetation types and soil properties significantly impact the structure and diversity of soil fungal communities in subtropical plantations. These changes in the fungal community may stimulate the soil nutrient cycle, contributing to the ecosystem sustainability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".