Spatial, temporal and technical variability in the diversity of prokaryotes and fungi in agricultural soils
Bibliographic record
Abstract
Several studies have shown that Illumina MiSeq high-throughput sequencing can be used to measure the diversity of prokaryotes and fungal communities that provide ecosystem functions in agricultural soils. Pedoclimatic properties of soils, together with cropping systems and agricultural management practices, are major drivers of soil microbiome diversity. Their effects must be quantified and compared to technical variability to improve the relevance of observed effects and the indicators that may result from them. This study was conducted: 1) To assess the effects of three sources of technical variability on the soil prokaryotes and fungal diversity; 2) To identify a source of technical variability that can be used as a threshold to better assess crop management effects; 3) To evaluate the effects of spatial and temporal variability compare to a technical threshold in three crop management contexts, potato, corn/soybean and grassland. Technical variability was evaluated in a basis of sampling, soil DNA extraction and amplicon sequencing source of variability. Spatial variability was evaluated using composite bulk soil cores at four sampling points covering 2500 m² per field. Geolocated soils were also collected on three sampling dates during the growing season to evaluate temporal variability. A technical variability threshold was determined for the soil DNA extraction variability with a delta of Shannon index of 0.142 and 0.390 and a weighted UniFrac distance of 0.081 and 0.364 for prokaryotes and fungi, respectively. We observed that technical variability was consistently similar or lower than the spatial and temporal variabilities in each of the microbial communities. Observed variability was greater for the diversity of fungi and the crop system has a strong effect on temporal and spatial variability.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".