Effects of burn severity on organic nitrogen and carbon chemistry in high-elevation forest soils
Bibliographic record
Abstract
Fire frequency and severity have increased in recent decades in the western United States, which have direct implications for the quantity and composition of soil organic matter (SOM). While the effects of wildfire on soil carbon (C) and inorganic nitrogen (N) have been well studied, little is known about the impacts of fire on soil organic N. Since organic N is the most abundant form of soil N in conifer forests and dominant source of plant N facilitated by symbiotic mycorrhizae and mineralization, better understanding of post-fire organic N chemistry will help address a critical gap in our understanding of fire effects on SOM. Here, we characterized changes to organic N chemistry across fire severity gradients resulting from two wildfires that burned lodgepole pine (Pinus contorta) forest along the Colorado/Wyoming border, USA. One representative gradient was selected for high-resolution analysis based on results from bulk data (total C and N, pH). Mineral soils were collected from two depths in low, moderate, and high severity burned areas and adjacent, unburned locations one year following the Ryan and Badger Creek fires. Nuclear magnetic resonance spectroscopy and ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry analysis showed that N content and aromaticity of water-extractable SOM (0-5 cm depth) increased with burn severity, while minimal changes to 5-10 cm depth were observed. Heterocyclic N species are generally higher in toxicity compared to their non-nitrogenated counterparts, which prompted soil toxicity measurements. Complementary Microtox® analysis revealed a positive relationship between increased fire severity and increased soil toxicity to Aliivibrio fischeri (microbial test species). These findings add to our molecular-level understanding of organic C and N responses to wildfire severity, with likely implications for nutrient cycling, forest recovery and water quality following severe wildfire.
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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".