Seasonal Responses of Extracellular Enzyme Activity and Microbial Biomass to Warming and Nitrogen Addition
Bibliographic record
Abstract
Soil microbial responses to climate warming in temperate regions may interact with the effects of increased atmospheric N deposition. In addition, the combined effects of these factors on microbial activity during the plant growing season may differ from the effects over winter, when reduced plant soil C inputs and soil freezing can alter microbial nutrient availability and demand. We examined seasonal changes in soil extracellular enzyme activity (EEA), microbial biomass C and N, and soil fungal and bacterial content in a warming and N addition experiment in a temperate old field. For EEA, we examined both hydrolases (organic C degrading enzymes, a chitinase and phosphatase) and ligninases (phenol oxidase and peroxidase). While both hydrolase and ligninase activities exhibited significant seasonal variation, EEA was unresponsive to the experimental treatments. Microbial biomass C increased with warming year round, however, and microbial biomass N increased with N addition but only over summer. Despite increased microbial biomass in response to warming, phosphatase was the only enzyme that exhibited a significant change in specific activity (enzyme activity per unit of microbial biomass) in response to warming. Likewise, soil fungal and bacterial biomass varied seasonally, but treatment effects on these variables were minimal. Overall, while the effects of N addition on microbial N varied seasonally, microbial responses were relatively insensitive to the warming and N addition treatments in our experiment. This insensitivity was unexpected given the large treatment effects on plant productivity and soil N dynamics documented during the same time frame in the field experiment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".