Forest soil acidification consistently reduces litter decomposition irrespective of nutrient availability and litter type
Bibliographic record
Abstract
Abstract Nitrogen (N), phosphorus (P) and acid deposition are co‐occurring in many ecosystems, likely with complex interactive effects on litter decomposition. Few studies have been conducted to distinguish the interactive effects of these three factors on forest litter decomposition. Thus, we performed a 5‐year litter decomposition experiment with N, P, acid addition in a temperate forest of Changbai Mountain in China, including four litter types from Pinus koraiensis, Quercus mongolica, Tilia amurensis and their mixtures. Our results showed that acid addition consistently reduced litter decomposition rate, irrespective of nutrient addition or litter types. In contrast, N and P addition had less impact on litter decomposition. Litter decomposition rate linearly reduced with decreasing soil pH, but positively increased with soil N availability. No relationship was found between soil P availability and litter decomposition. Soil enzyme activity played a key role in regulating litter decomposition response, such as acid phosphatase, xylosidase, N‐cacetyl‐b‐D‐glucosaminidase and α‐1,4 glucosidase. Besides, low‐quality litter (i.e. high C concentration, C:N and C:P ratio) amplified the negative effect of soil acidification on litter decomposition. This study suggests that soil acidification consistently decelerates litter decomposition in temperate forests, which is independent of soil nutrient availability and litter types. The intensifying soil acidification with continuous N deposition in the future will greatly reduce litter nutrient return to soil, increasing the risk of multiple soil nutrient limitation. A free Plain Language Summary can be found within the Supporting Information of this article.
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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) | 0.000 | 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 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".