Impacts of land‐use changes on the variability of microbiomes in soil profiles
Bibliographic record
Abstract
BACKGROUND: The conversion of arable land to grassland and/or forested land is a common strategy of restoration because the development of plant communities can inhibit the erosion of soil, increase biodiversity and improve associated ecosystem services. The vertical profiles of microbial communities, however, have not been well characterized and their variability after land conversion is not well understood. We assessed the effects of the conversion of arable land (AL) to grassland (GL) and forested land (FL) on bacterial communities as old as 29 years in 0-200-cm profiles of a Chinese Mollisol. RESULTS: The soil in AL has been a stable ecosystem and changes in the assembly of soil microbiomes tended to be larger in the topsoil. The soil properties and microbial biodiversity of arable land were larger following revegetation and reforestation. The conversion caused a more complex coupling among microbes, and negative interactions and average connectivity were stronger in the 0-20-cm layers in GL and in the 20-60-cm layers in FL. The land use dramatically influenced the assembly of the microbial communities more in GL than AL and FL. The bacterial diversity was an important component of soil multinutrient cycling in the profiles and microbial functions were not as affected by changes in land use. CONCLUSION: The spatial variation of the microbiomes provided critical information on below-ground soil ecology and the ability of the soil to provide crucial ecosystem services. © 2021 Society of Chemical Industry.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".