Soil microbiomes in degraded grasslands: Assembly, function, and application
Bibliographic record
Abstract
Abstract Grassland ecosystems are pivotal to sustaining multiple ecosystem functions and services like climate regulation, carbon sequestration, and grass production. However, the global degradation of grasslands is intensifying under the combined impacts of climate change (e.g., extreme drought) and anthropogenic activities (e.g., overgrazing). The exploration of microorganism presence and roles in degraded grasslands has achieved substantial progress. Here, we review the literature on soil microbes in degraded grasslands over the past decade, with emphasis on community response, microbial‐mediated nutrient cycling processes, and potential application for restoration. Grassland degradation diminishes soil microbial diversity by reducing resource availability, resulting in the homogenization of microbial communities. However, these effects remain controversial in the context of patchy degradation. Meanwhile, degradation typically triggers the loss of key microbial species or some functional genes, coupled with suppressed activity of nutrient cycling‐related enzymes, and may also promote certain processes like the decomposition of complex organic matter (e.g., lignin). We further evaluate current advances and limitations in microbial inoculant applications for grassland restoration. Some future directions in degraded grasslands are advocated, including plant–soil–microbe interaction analysis, degradation trend prediction using microbial dynamic data, and microbial multifunctional inoculant application. Promising restoration strategies, integrating metabolite identification and targeted microbiome modification, offer valuable pathways for future research and practical implementation under global change scenarios.
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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 | 0.002 | 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) | 0.000 | 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".