Soil fungal communities of montane natural secondary forest types in China
Bibliographic record
Abstract
Distinctive plant communities may provide specific physical and chemical properties with soils by specific litters and root exudates to exert effects on soil microorganisms. Past logging activities in the Qinling Mountains induced diverse natural secondary forest types (NSFTs). How these recovered NSFTs regulate patterns of soil microbial communities remain limited. In the study, we used terminal-restriction fragment length polymorphism (T-RFLP) to precisely determine forest type-specific soil fungal diversity and composition in five NSFTs. Our results indicated that NSFTs had significant impacts on the soil fungal communities. The most diverse fungal species were found in the Armand pine (Pinus armandi) and Chinese pine (Pinus tabulaeformis) forest soils, followed by sharptooth oak (Quercus aliena var. acuteserrata) and Chinese pine-sharptooth oak forest soils, the wilson spruce (Picea wilsonii) forests had the lowest soil fungal diversity. The analyses of community composition suggested that the fungal communities of Armand pine forest soils were similar to those of Chinese pine forest soils, while other communities prominently differed from each other. Stepwise multiple regression analysis revealed that soil silt, clay, pH, and ammonium nitrogen had intimate linkages with soil fungal diversity. Furthermore, the patterns of soil fungal communities were strongly governed by the specific soil environments of the tested NSFTs, as described by canonical correspondence analysis (CCA). Finally, our study showed that soil fungal communities may be mediated by NSFTs via specific soil edaphic status. Hence, such a comparable study may provide fundamental information for fungal diversity and community structure of natural forests and assist with better prediction and understanding how soil fungal composition and function alter with forest type transformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".