Litterfall quality modulates soil ammonium and nitrate supply through altering microbial function in bamboo encroachment of broadleaf forests
Why this work is in the frame
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Bibliographic record
Abstract
Plant invasion can change soil microbial function and nitrogen (N) supply through the input of litter with different qualities and quantities, among other factors. However, the underlying mechanism for litter quality to regulate soil N mineralization via microbial function with plant invasion remains unclear. We examined the effect of litterfall with contrasting chemical properties on soil net ammonification and nitrification rates, enzyme activity, and associated microbial function using reciprocal litterfall transplanting between a natural broadleaf forest and a Moso bamboo forest formed after the encroachment of a natural broadleaf forest. Our results showed that, on average, litterfall removal decreased ammonification rates by 33.6% but increased nitrification rates by 137.8%, while litterfall replacement increased nitrification rates by 55.9% in the natural broadleaf forest. Litterfall removal increased ammonification rates by 33.9%, while litterfall replacement increased ammonification rates by 42.1% at the end of the experiment in the bamboo forest. Structural equation modeling revealed that higher quality litterfall (higher cellulose content and lower lignin/N ratio) from the broadleaf forest enhanced net ammonification by altering fungal composition and increasing their ligninolytic capacity. However, the lower quality litterfall from the bamboo forest increased net nitrification rates through altering bacterial community and increasing urease activity. Our results indicate that the increased N supply resulting from litterfall replacement could counteract the low NH4+ availability in the bamboo forest soil, but not in the broadleaf forest soil. The contrasting modulation effects of litterfall quality on soil mineral N production would have cascading effects on bamboo encroachment and forest succession with implications for ecosystem management.
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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 | 0.000 | 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) | 0.000 | 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 it