Microbial traits dictate soil necromass accumulation coefficient: A global synthesis
Bibliographic record
Abstract
Abstract Aim The accumulation of microbial necromass carbon has gained increasing attention due to its slow decomposition. However, it remains unclear what induces the accumulation of microbial necromass carbon via reiterated community turnover on large spatial scales. This study explores the characteristics of soil necromass carbon accumulation in terrestrial ecosystems. Location Global. Time Period Contemporary (1999–2022). Major Taxa Studied Soil microorganisms. Methods A dataset was compiled using 993 observations from 82 peer‐reviewed papers on the effects of coefficients of microbial necromass accumulation (hereafter NAC) in equilibrium. Linear mixed‐effect models and structural equation models were used to ascertain the controlling factors of the NAC. Results The average NAC was higher in croplands (28.2) and forests (26.8) than in grasslands (21.1). Edaphic factors seemingly affected the NAC, which was lower in soils with high pH and clay content on a global scale. Biotic factors, particularly those related to living microorganism abundance and microbial biomass nitrogen content, were the pivotal drivers of NAC and accounted for approximately 43.6% of its geographic variability. More organic carbon was likely to be preserved in soil with a higher NAC, regardless of ecosystem type. Conclusions Novel findings regarding the overriding controls for the living microorganism abundance and microbial biomass nitrogen that drive the NAC highlight an urgent need for viable strategies to manipulate microbial attributes for enhancing carbon sequestration.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".