Grazing-N addition interactions drive soil carbon priming and balance via bacterial assimilation in a meadow steppe
Bibliographic record
Abstract
Grassland carbon storage depends on microbial-mediated interactions between grazing and nitrogen (N) addition, which regulates the balance between soil organic carbon (SOC) retention and priming effects. However, uncertainties regarding these interactive mechanisms constrain projections of SOC vulnerability under global change. We conducted a factorial field experiment involving grazing and N addition in a Leymus chinensis meadow in northeastern China. In the fifth year of the experiment, we collected soil to conduct a 70-day soil incubation combined with labile carbon (glucose) addition to examine the effects of the grazing and N addition treatments soil carbon priming and carbon retention. Grazing consistently increased priming effects regardless of N addition. In contrast, N addition strongly reduced priming by 41.0% in ungrazed plots but had minimal increase effects (3.2%) under grazing. Mechanistically, bacterial glucose assimilation capacity primarily mediated grazing-dependent N effects on priming, explaining 65.0% of the variation and correlating positively with priming intensity. Grazing notably decreased the net SOC balance (35.7 mg kg-1 soil) and diminished the beneficial effect of N addition on SOC (+79.6% in ungrazed vs. +12.3% in grazed plots). Priming effects and bacterial glucose assimilation were dominant drivers of SOC responses under grazing, exhibiting negative correlations with net SOC balance. Synthesis and applications: Our results show that grazing-induced bacterial dominance in carbon assimilation alters priming effects and net soil carbon balance under N addition, offsetting potential carbon sequestration benefits by accelerating native organic matter decomposition. Thus, microbial carbon assimilation capacity, particularly bacterial substrate assimilation, may serve as an indicator of SOC vulnerability under global change.
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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 | 0.000 |
| 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".