Elevated temperature shifts soil N cycling from microbial immobilization to enhanced mineralization, nitrification and denitrification across global terrestrial ecosystems
Bibliographic record
Abstract
Abstract We assessed the response of soil microbial nitrogen (N) cycling and associated functional genes to elevated temperature at the global scale. A meta‐analysis of 1,270 observations from 134 publications indicated that elevated temperature decreased soil microbial biomass N and increased N mineralization rates, both in the presence and absence of plants. These findings infer that elevated temperature drives microbially mediated N cycling processes from dominance by anabolic to catabolic reaction processes. Elevated temperature increased soil nitrification and denitrification rates, leading to an increase in N 2 O emissions of up to 227%, whether plants were present or not. Rates of N mineralization, denitrification and N 2 O emission demonstrated significant positive relationships with rates of CO 2 emissions under elevated temperatures, suggesting that microbial N cycling processes were associated with enhanced microbial carbon (C) metabolism due to soil warming. The response in the abundance of relevant genes to elevated temperature was not always consistent with changes in N cycling processes. While elevated temperature increased the abundances of the nirS gene with plants and nosZ genes without plants, there was no effect on the abundances of the ammonia‐oxidizing archaea amoA gene, ammonia‐oxidizing bacteria amoA and nirK genes. This study provides the first global‐scale assessment demonstrating that elevated temperature shifts N cycling from microbial immobilization to enhanced mineralization, nitrification and denitrification in terrestrial ecosystems. These findings infer that elevated temperatures have a profound impact on global N cycling processes with implications of a positive feedback to global climate and emphasize the close linkage between soil microbial C and N cycling.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".