Unfolding the Potential of Soil Microbial Community Diversity for Accumulation of Necromass Carbon at Large Scale
Bibliographic record
Abstract
Microorganisms are the main drivers of soil organic carbon (SOC) formation, especially through the accumulation of microbial necromass C. It is unclear, however, how microorganisms mediate the accumulation of necromass in soil because microbial communities are prohibitively diverse. To bridge this knowledge gap, biomarkers of microbial cell walls (amino sugars) were combined with high-throughput sequencing, spanning a 900 km climatic gradient through the Loess Plateau. The cropland and three restoration types (grassland, shrubland, and forestland) were included, and 291 samples were collected. Necromass C, microbial diversity, and enzyme activities showed the same trend after vegetation restoration (from cropland to forestland). Soil pH, clay, microbial biomass C, and α-1,4-glucosidase were the strong predictors for both bacterial and fungal necromass C. There was a strong positive linear relationship that existed between bacterial necromass C and diversity and also between fungal necromass C and diversity (p < 0.01), pointing to the strong links between microbial diversities and residues. Specifically, necromass C was strongly correlated with dominant microbial taxa, suggesting that these taxa might control the variation of necromass and other metabolic residues. The relative abundances of Actinobacteria, Proteobacteria, and Bacteroidetes gradually increased after vegetation restoration, and changed from oligotrophic to copiotrophic groups. It means that vegetation restoration promoted opportunistic and resilient microbial taxa that may have copiotrophic or fast-response characteristics to increase the accumulation of necromass C and potentially contribute to soil C sequestration in these systems. In this regard, vegetation restoration governs SOC storage by shaping the unique dominant microbial communities, facilitating the accumulation of necromass C. This research enhances our understanding of the survival strategies of microbial life and suggests greater contribution to necromass than previously recognized for soil microbiomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".