Deciphering factors driving soil microbial life‐history strategies in restored grasslands
Bibliographic record
Abstract
Abstract In macroecology, the concept of r‐ and K‐strategy has been widely applied, yet, there have been limited studies on microbial life‐history strategies in temperate grasslands using multiple sequencing approaches. Total phospholipid fatty acid (PLFA) analysis, high‐throughput meta‐genomic sequencing, and GeoChip technologies were used to examine the changes in microbial life‐history traits in a chronosequence of restored grasslands (1, 5, 10, 15, 25, and 30 years since restoration). Grassland restoration increased the relative abundances of Actinobacteria, Proteobacteria, and Bacteroidetes but reduced the relative abundances of Acidobacteria, Planctomycetes, and Chloroflexi. PLFA analysis revealed that grassland restoration reduced the fungi:bacteria and Gram‐positive:Gram‐negative bacteria ratios. Combined with the meta‐genomic data, we found that grassland restoration shifted microorganisms from oligotrophic (K‐) to copiotrophic (r‐) groups, consistent with the increased rRNA operon copy number of the microbial community. Structural equation modeling showed that soil properties positively ( p < 0.05) while plant properties negatively ( p < 0.05) affected microbial life‐history traits. We built a framework to highlight the importance of plant and soil properties in driving microbial life‐history traits during grassland restoration. Finally, by incorporating meta‐genomic and other microbiological data, this study showed that microbial life‐history traits support the idea that rRNA operon copy number is a trait that reflects resource availability to soil microorganisms.
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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 | 0.000 |
| 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".