Restoration in degraded subtropical broadleaved forests induces changes in soil bacterial communities
Bibliographic record
Abstract
Soil resident bacterial communities are involved in myriad key processes that facilitate ecosystem functionality. However, our understanding of their diversity and compositional dynamics following ecological restoration, and the main factors that influence them, remains inadequate. We employed a chronosequence (0–1, 5–6, 11–12, 20–24, and 28–34 years since restoration) to examine the dynamic changes in soil bacterial diversity and composition, as well as the essential factors that affected them since the cessation of anthropogenic disturbances (e.g., recurring fuelwood collection and domestic animal grazing), and used old-growth forests as a reference in the subtropical forests of Eastern China. We found that soil bacterial diversity increased with time since restoration, and community compositions shifted toward being similar to those of old-growth forests over time. However, the recovery process was prolonged since the significant difference in soil bacterial diversity between degraded and restored forests did not occur until after 24 years since restoration. Multivariate analysis using multiple-response permutation procedures indicated the soil bacterial communities were compositionally distinct between degraded, restored, and old-growth forests. An analysis of indicator species revealed that forests at the early stage of recovery times supported Rokubacteria and Actinobacteria, while old-growth forests were distinguished by Chlamydiae. Soil carbon, microbial biomass carbon, soil water content, and microbial biomass nitrogen recovered over time and became increasingly akin to those of old-growth forest soils. Soil carbon, soil water content, and soil pH could explain 84.5% of the variations in bacterial community dynamics following restoration. Overall, this study revealed a prolonged recovery process of the community structures of soil bacteria (e.g., diversity, composition, and phylum abundance) following restoration, which was coupled with changes in soil properties in subtropical forests of China.
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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 | 0.000 | 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 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".