Spatial heterogeneity of soil carbon exchanges and their drivers in a boreal forest
Bibliographic record
Abstract
Boreal forests have a large impact on the global greenhouse gas balance and their soils constitute an important carbon (C) reservoir. Mature boreal forests are typically a net CO2 sink, but there are also examples of boreal forests that are persistent CO2 sources. The reasons remain often unknown, presumably due to a lack of understanding of how biotic and abiotic drivers interact to determine the microbial respiration of soil organic matter (SOM). This study aimed at identifying the main drivers of microbial SOM respiration and CO2 and CH4 soil chamber-fluxes within dry and wet sampling areas at the mature boreal forest of Norunda, Sweden, a persistent net CO2 source. The spatial heterogeneity of the drivers was assessed with a geostatistical approach combined with stepwise multiple regression. We found that heterotrophic soil respiration increased with SOM content and nitrogen (N) availability, while the SOM reactivity, i.e., SOM specific respiration, was determined by soil moisture and N availability. The latter suggests that microbial activity was N rather than C limited and that microbial N mining might be driving old-SOM decomposition, which was observed through a positive correlation between soil respiration and its δ13C values. SOM specific heterotrophic respiration was lower in wet than in dry areas, while no such dependencies were found for chamber-based soil CO2 fluxes, implying that oxygen depletion resulted in lower SOM reactivity. The chamber-based soil CH4 flux differed significantly between the wet and dry areas. In the wet area, we observed net CH4 emission that was positively related to soil moisture and NH4+-N content. Taken together, our findings suggest that N availability has a strong regulatory effect on soil CO2 and CH4 emissions at Norunda, and that microbial decomposition of old-SOM to release bioavailable N might be partly responsible for the net CO2 emission at the site.
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 | 0.001 |
| 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 | 0.001 |
| 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".