Structure and function of the topsoil microbiome in Chinese terrestrial ecosystems
Bibliographic record
Abstract
While soil microorganisms underpin terrestrial ecosystem functioning, how their functional potential adapts across environmental gradients remains poorly understood, particularly for ubiquitous taxa. Employing a comprehensive metagenomic approach across China's six major terrestrial ecosystems (41 topsoil samples, 0-20 cm depth), we reveal a counterintuitive pattern: oligotrophic environments (deserts, karst) harbor microbiomes with significantly greater metabolic pathway diversity (KEGG) compared to resource-rich ecosystems. We provide a systematic catalog of key functional genes governing biogeochemical cycles in these soils, identifying: 6 core CAZyme genes essential for soil organic carbon (SOC) decomposition and biosynthesis; 62 nitrogen (N)-cycling genes (KOs) across seven critical enzymatic clusters; 15 sulfur (S)-cycling genes (KOs) within three key enzymatic clusters. These functional gene abundances exhibit distinct, geography-driven clustering patterns, strongly correlated with eight environmental drivers (latitude, NDVI, pH, EC, SOC, TN, C:N ratio, and MAP). This work provides a predictive framework and actionable genetic targets (e.g., specific CAZyme, N/S cycling genes) for potentially manipulating soil microbiomes to enhance ecosystem resilience and biogeochemical functions under stress.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".