Changes in rhizosphere bacterial and fungal community composition with vegetation restoration in planted forests
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Soil microbial communities affect nutrient cycling and ecosystem functioning. However, the variations in microbial diversity and community composition within degraded landscapes remain unclear. Using high‐throughput sequencing of bacterial 16S ribosomal RNA genes and internal transcribed spacer fungal sequences, we investigated the rhizosphere microbial diversity and community of coniferous Pinus tabulaeformis Carr. forests in degraded lands across a chronosequence that spanned over 60 years (10, 25, 40, and 60 years since restoration, four forest stands). We found significant differences in soil bacterial and fungal communities among stand ages. Actinobacteria , Proteobacteria , and Acidobacteria dominated the rhizosphere, whereas Basidiomycota, Ascomycota , and Zygomycota prevailed as fungal components. With stand development, bacterial diversity decreased, but fungal diversity increased. Nonmetric multidimensional scaling analysis separated bacterial community clusters well by stands. Fungal community clusters of 25‐ and 60‐year‐old stands overlapped. The dominant bacteria Acidobacteria showed the highest relative abundance at the 40‐year‐old stands. Soil microbial communities correlated significantly with the macro‐nutrients (soil organic carbon, total nitrogen, and total phosphorous). Additionally, the relative abundance of Acidobacteria at the phylum level correlated positively with soil total phosphorous; Deltaproteobacteria at the class level correlated positively with soil organic carbon and total nitrogen. Thus, restoring vegetation in degraded temperate forests enhanced some macronutrients and influenced microbial communities. Our results revealed that restoring vegetation in degraded lands decreased the diversity of bacterial communities over time. In contrast, the soil fungal diversity increased after restoration, and fungal communities in the 25‐ and 60‐year‐old forest stands overlapped on degraded soils.
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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 it