Complex responses of soil prokaryotes, fungi and protists to prairie restoration on retired agricultural lands
Bibliographic record
Abstract
Abstract Restoring native ecosystems on marginal croplands has many benefits but the impacts on belowground biodiversity are less clear, in part because the limiting factors regulating soil biota are complex and poorly described. Here, we studied how grassland prairie restoration of marginal croplands affected the diversity and composition of soil microbiota on 5 conventional farms from Ontario, Canada. Soil samples (0-15 cm) were collected from annually cultivated fields and adjacent planted perennial grassland where cultivation and chemical inputs had ceased several years previously. Following DNA extraction, we estimated bacterial and fungal abundance using quantitative PCR, and microbial diversity of prokaryotes, fungi and protists using amplicon high-throughput sequencing. Under both land uses, prokaryotic communities were dominated by Proteobacteria, Actinobacteria and Acidobacteria, fungal communities by Ascomycota, and protist communities by Rhizaria (TSAR), Evosea (Amoebozoa) and Chlorophyta (Archaeplastida). Prairie restoration did not have a consistent effect on soil microbial abundance, richness or evenness, which responses varied across farms. Microbial genetic and taxonomic community composition ( i.e. , sequence variant and genus level) were affected by land use, farm and the interaction between these two factors. Generally, prairie soils had higher relative abundance of Latescibacterota, Desulfobacterota, Acidobacteriota and Glomeromycota, and lower of Deinococcota, Chytridiomycota and Amoebozoa_X. In terms of differentially abundant fungal genera, prairies promoted more fungal plant symbionts, less saprotrophs and no plant pathogens. Interkingdom networks revealed changes in potential microbe-microbe associations with prairie restoration, with only 8 associations in common between land uses. The relationship between soil microbial diversity and physicochemical properties varied across microbial groups, diversity metrics and land uses. Our results evidence the complexity associated with restoring soils from agricultural land to natural ecosystems, with unspecified farm-specific factors ( e.g. , soil type, prairie species, management history) strongly modulating the response of different microbial groups and variables.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".