Microbial co-occurrence network analysis of soils receiving short- and long-term applications of alkaline treated biosolids
Bibliographic record
Abstract
Agricultural soils are inherently disturbed systems where organic matter additions are considered to enhance microbial community structure and resilience. High-throughput sequencing of community was applied to soils receiving annual applications of an alkaline stabilized biosolid (ATB), at four increasing rates over 10 years, as an environmental stressor in contrast to a one-time application of ATB ten years prior. Bacterial community structure was more greatly influenced by annual ATB applications relative to fungi and eukaryotes. Specifically, higher relative abundances of Proteobacteria, Acidobacteria, Bacteroidetes, and Chloroflexi were measured in annual ATB rates relative to the single ATB rates and the control. High rates of annual ATB applications resulted in lower bacterial alpha-diversity, as well as fungal and eukaryotic Shannon diversity, but single ATB or lower rates of ATB applied annually showed increased alpha -diversity relative to the control. Soil microbiome responses to annual ATB and single ATB rates were also examined using co-occurrence network analysis. High rates and frequency of ATB application resulted in a decrease in network interactions, lower average number of neighbors, and reduced network density compared to control soils. A concomitant increase in network diameter and characteristic path length further suggests annual additions of ATB led to a more adapted, but less cooperative, state in the microbiome. The data suggest a more universal functional response of microbiomes to the stressors compared to community structure and local diversity. In particular, beta-analysis and network analysis were both able to resolve significant effects on soil microbiomes 10 years post-application of low rates of ATB. Community complexity and stability were increased by single low rate of ATB additions and decreased by single high rate and annual moderate rates of ATB additions. These results provide insights into the effects that ATB additions have on soil community after only one-time use and after annual additions over a decade.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".