Environmental and edaphic drivers of bacterial communities involved in soil N-cycling
Bibliographic record
Abstract
The cycling of N in soil is supported both directly and indirectly by numerous microbial processes. These processes affect ecosystem fertility, but can also generate forms of N which have detrimental environmental impacts, such as N2O. Understanding drivers of biological communities involved in key N-transformations is therefore of much interest. The effects of physicochemical and environmental properties on the relative size (abundance within total DNA pool) of biological communities involved in 3 key N transformations were investigated. Soils from 14 locations spanning a rainfall gradient across 3 agricultural regions (Clare, Mallee, Balaclava) were sampled, with samples taken from the surface and at depth from each site. Based on PCA of physicochemical and environmental properties, the soils fell into 2 distinct groupings: Clare and Mallee + Balaclava ‘types’. The abundance of functional genes involved in N2 fixation (nifH), ammonia oxidation (amoA), and nitrate reduction (narG) was quantified in DNA extracted from the soils using real-time PCR. The abundance of the nifH gene varied significantly with site (P = 0.03) but not depth, and no regional association with nifH gene abundance was found. Multivariate analysis indicated that the abundance of nifH was positively correlated with soil total C (ρ = 0.382; P = 0.006). Similarly, the abundance of narG varied with site (P < 0.001) and not soil depth. The abundance of narG was positively correlated with increasing rainfall (ρ = 0.417; P = 0.002). The abundance of amoA did not significantly vary between soils, but significantly decreased with soil depth (P = 0.006). The abundance of amoA was negatively correlated with soil electrical conductivity and positively with organic C (combined ρ = 0.44; P = 0.003). Whereas there was no relationship between the abundance of nifH and amoA or narG, the abundance of amoA was positively correlated with the abundance of narG (P < 0.001). These results indicate that the abundance of the N cycling genes is independently affected by different physicochemical or environmental properties. The interactions between soil, environment, and the functionally significant biological communities they support are complex. To gain fuller understanding of soil N cycling, the ecology of the various biological components affecting N-transformations must be investigated simultaneously.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".