Responses of Ammonia-Oxidising Bacterial Communities to Nitrogen, Lime, and Plant Species in Upland Grassland Soil
Bibliographic record
Abstract
Agricultural improvement of seminatural grasslands has been shown to result in changes to plant and microbial diversity, with consequences for ecosystem functioning. A microcosm approach was used to elucidate the effects of two key components of agricultural improvement (nitrogen addition and liming) on ammonia-oxidising bacterial (AOB) communities in an upland grassland soil. Plant species characteristic of unimproved and improved pastures ( A. capillaris and L. perenne ) were planted in microcosms, and lime, nitrogen (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mtext>NH</mml:mtext></mml:mrow><mml:mtext>4</mml:mtext></mml:msub><mml:msub><mml:mrow><mml:mtext>NO</mml:mtext></mml:mrow><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math>), or lime plus nitrogen added. The AOB community was profiled using terminal restriction fragment length polymorphism (TRFLP) of the amoA gene. AOB community structure was largely altered by<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mtext>NH</mml:mtext></mml:mrow><mml:mtext>4</mml:mtext></mml:msub><mml:msub><mml:mrow><mml:mtext>NO</mml:mtext></mml:mrow><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math>addition, rather than liming, although interactions between nitrogen addition and plant species were also evident. Results indicate that nitrogen addition drives shifts in the structure of key microbial communities in upland grassland soils, and that plant species may play a significant role in determining AOB community structure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.003 |
| Scholarly communication | 0.000 | 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 teacher head, 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".