Land use in the Prairie Pothole Region influences the soil bacterial community composition and relative abundance of nitrogen cycling genes
Bibliographic record
Abstract
The undulating topography of Prairie Pothole Region of North America creates spatial and temporal variability in soil moisture and nutrient levels, affecting microbial community processes and greenhouse gas emissions. By identifying differences in soil bacterial and archaeal community composition and the abundance of nitrogen cycling genes in permanent cover versus annual crop land over two growing seasons (2017 and 2018), we were able to assess the effects of topography and land use on the functional capacity of the soil microbiome. Permanent grassland cover was associated with higher bacterial diversity in upland positions and lower diversity in low-lying depressions. Bacterial community composition was also significantly different between cultivated and permanent cover at all points along the topographic slope, with the largest effects seen in the footslope and backslope positions. Compared to permanent cover, soil from annual cropland had consistently more abundant nitrifiers, including Nitrospira in the toeslope and backslope, and Nitrososphaeraceae in the shoulder and knoll samples while soils from permanent cover had a greater abundance of several Alphaproteobacteria from Rhodospirillales and Hyphomicrobiaceae across multiple upland positions. Upland soils from annual cropland also had consistently higher abundance of both bacterial and archaeal ammonia oxidizing ( amoA) genes and a higher ratio of nirK: nirS genes compared to those from permanent cover. These differences in microbial community composition were associated with higher N2O and CO2 emissions in upland soils in annual cropland; however, there were no differences in GHG emissions between the two systems in low-lying positions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".