Distinct roles of plant and microbial communities in ecosystem multifunctionality during grassland degradation and restoration
Bibliographic record
Abstract
Understanding the mechanisms of grassland degradation and restoration is critically important for maintaining the health of grasslands, which occupy one-third of the planet’s land surface. Extensive research has focused on the impacts of plant communities on ecosystem multifunctionality (EMF) during grassland degradation and restoration, but soil microbial communities have been left out. This project investigated the roles of plant and soil microbial communities in regulating EMF across five grassland ecosystems spanning a 3,500 km transect. We quantified EMF based on eight ecosystem functions and assessed its dynamics during seven phases: natural grassland, moderate degradation, heavy degradation, severe degradation, short-term fencing, medium-term fencing, and long-term fencing. Our results showed that during grassland degradation, bacterial diversity declined more slowly than fungal and plant diversity, and EMF decline was primarily driven by reductions in plant diversity and the abundance of perennial forbs. During grassland restoration, the bacterial community recovered much faster than the plant and fungal communities, emerging as the primary driver of EMF recovery. Structural equation modeling identified plant and microbial communities as the most important predictors of EMF, even after accounting for climate and soil properties. Soil bacterial diversity and the relative abundance of dominant bacterial taxa (e.g., Actinobacteria , Proteobacteria , and Verrucomicrobia ) were key determinants of EMF recovery. Functional redundancy and resilience of these dominant bacterial taxa enabled consistent EMF recovery across diverse climate conditions. This study provides valuable insights into the distinct roles that soil microbial and plant communities play in driving EMF dynamics during grassland degradation and restoration. Our findings highlight the dominant role of soil bacteria in grassland restoration, suggesting that future management practices should prioritize promoting soil bacterial communities to enhance grassland recovery.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".