Decoupled richness of generalist anaerobes and sulphate‐reducing bacteria is driven by <scp>pH</scp> across land uses in temperate soils
Bibliographic record
Abstract
Abstract Sulphate‐reducing bacteria (SRB) represent a key biological component of the global sulphur (S) cycle and are common in soils, where they reduce SO 4 2− to H 2 S during the anaerobic degradation of soil organic matter. The factors that regulate their distribution in soil, however, remain poorly understood. We sought to determine the ecological patterns of SRB richness within a nationwide 16S metabarcoding dataset. Across 436 sites belonging to seven contrasting temperate land uses (e.g., arable, grasslands, woodlands, heathland and bog), SRB richness was relatively low across land uses but greatest in grasslands and lowest in woodlands and peat‐rich soils. There was a shift in dominant SRB taxa from Desulfosporosinus and Desulfobulbus in arable and grassland land uses to Desulfobacca in heathland and bog sites. In contrast, richness of other generalist anaerobic bacterial taxa found in our dataset (e.g., Clostridium , Geobacter and Pelobacter ) followed a known trend of declining richness linked to land‐use productivity. Overall, the richness of SRBs and anaerobes had strong positive correlations with pH and sulphate concentration and strong negative relationships with elevation, soil organic matter, total carbon and carbon‐to‐nitrogen ratio. It is likely that these results reflect the driving influence of pH and competition for optimal electron acceptors with generalist anaerobic bacteria on SRB richness. Highlights Sulphate‐reducing bacteria (SRB) are key but rare soil biota that may compete with other anaerobes As UK sulphur deposition rates fall, local populations of SRB may also decline in soils Sulphate concentrations were higher in arable and wooded sites, not at higher elevation as expected SRB richness was lower than generalist anaerobes, with peaks in grasslands and a drop in lowland woods.
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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".