Soil Element Stoichiometry Drives Bacterial Community Composition Following Thinning in A Larix Plantation in the Subalpine Regions of Northern China
Bibliographic record
Abstract
It is well established that forest thinning alters aboveground plant community composition and soil resource availability. However, how it regulates the composition and diversity of belowground microbial communities remains unclear. To quantify the effects of thinning on soil bacterial groups and the underlying mechanisms of these effects, this research was conducted in a Larix principis-rupprechtii Mayr. plantation with various thinning intensities, including a control (0% tree removal), a low-intensity treatment (15% tree removal), a medium-intensity treatment (35% tree removal), and a high-intensity treatment (50% tree removal). Compared to the control, the medium and high intensity thinning treatments significantly improved soil moisture, nutrient concentrations (including soil total carbon, nitrogen, phosphorus, and ammonium nitrogen), microbial biomass, and elemental stoichiometry ratios. The abundance and diversity of bacterial communities peaked in the medium-intensity treatment. Thinning also had strong effects on dominant bacterial groups at the phylum level. For instance, Bacteroidetes and Nitrospirae were significantly increased in the medium-intensity treatment (MIT), while the Gemmatimonadetes were significantly decreased in the low-intensity treatment (LIT). Combining Spearman correlation analysis and redundancy analysis demonstrated that thinning could facilitate the assembly of unique bacterial communities, and these shifts in microorganisms could probably be attributed to corresponding changes in soil resource stoichiometry. In conclusion, this study provides novel evidence that rational thinning could promote belowground bacterial community diversity and that elemental stoichiometry is an important indicator in shaping forest soil bacterial communities.
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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".