Chronic Warming and Nitrogen-Addition Alter Soil Organic Matter Molecular Composition Distinctly in Tandem Compared to Individual Stressors
Bibliographic record
Abstract
Forest soils are major reservoirs of carbon (C), but these stores are threatened by increasing global temperatures and atmospheric nitrogen (N) deposition. These environmental stressors can alter soil microbial communities and soil organic matter (SOM) biogeochemistry through a variety of mechanisms. To investigate the impact of chronic warming, N-addition, and simultaneous warming and N-addition (warming + N) on forest soils, soil samples from the Harvard Forest Soil Warming and Nitrogen Addition (SWaN) experiment were analyzed after 14 years. Elemental analysis, targeted compound analysis by gas chromatography–mass spectrometry, and 13 C nuclear magnetic resonance (NMR) spectroscopy were used to analyze changes in SOM in both the organic and mineral (0–10 cm) soil layers. Overall, changes in the molecular composition and microbial biomass were observed, but the extent of differences was unique to warming, N-addition, and warming + N treatments. Specifically, N-addition slowed SOM decomposition as measured via solid-state 13 C NMR, while warming and warming + N accelerated SOM decomposition. Continued SOM decomposition after 14 years with warming + N signified a pronounced change to observations made after 4 and 10 years of experimental treatment. This is also demonstrative of how a two-factor approach leads to a unique molecular-level response that cannot be predicted from experiments with individual stressors alone. This study emphasizes the need to observe environmental stressors in tandem using a combination of molecular-level approaches to obtain a comprehensive understanding of how persistent anthropogenic activity will fundamentally alter forest soil systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".