Deciphering the Complex Interactions between Litter Inputs and Microbial Responses in Modulating Long-Term Soil Organic Matter Dynamics
Bibliographic record
Abstract
Natural climate solutions that focus on increasing carbon in forests rely on the potential for additional carbon that may be incorporated into soil organic matter (SOM). The fate of soil carbon in temperate forests remains uncertain due to the complex role of microbes and their regulation of carbon flows in soils, especially with the addition of extra litter. We identified comprehensive molecular-level evidence that revealed shifts in SOM composition and microbial communities after 30 years of added litter in a temperate deciduous forest. Chronic litter addition failed to add new soil carbon after 30 years and correlated with reorganization in microbial community composition and altered carbon use. Excluding detrital inputs decreased soil carbon content, resulting in enhanced SOM decomposition and shifts toward specific bacterial groups (such as oligotrophs) that can utilize less energetically favorable carbon substrates that are typically more recalcitrant. Collectively, we found that microbial communities shifted in composition and altered carbon use strategies and traits, which aligned with changes to the molecular composition of SOM. Finally, this work demonstrates that in mesic temperate forests, decadal increases in litterfall, resulting from increased ecosystem productivity or management, may not offset soil carbon losses from climate change nor enhance 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 | 0.000 | 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 | 0.001 |
| 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".