Fire and harvesting impacts on forest floor and water extractable organic matter chemical composition in the Canadian Rocky Mountains
Bibliographic record
Abstract
Forests globally store a significant portion of terrestrial organic carbon, where two-thirds of that carbon is stored as soil organic matter (SOM). The dissolved organic matter (DOM) in soils plays a crucial role in carbon and nutrient cycling. Understanding the dynamics of DOM and SOM in forest ecosystems, especially their chemical composition is crucial for managing water quality and ecosystem health, particularly in response to land disturbances. Water extractable organic matter (WEOM) represents a fraction of DOM obtained through laboratory extraction and has been proposed as an early and sensitive indicator of shifts in SOM dynamics. In this study, different analytical techniques were used to assess the chemical composition of WEOM in forest soils (forest floor and underlying mineral soil horizon) impacted by clear-cut harvesting and forest fire. The research took place in two pairs of watersheds within the southern Canadian Rocky Mountains. The analytical techniques employed were solid state magic-angle spinning 13C nuclear magnetic resonance (NMR) in both direct polarization (DP) and cross polarization (CP) pulse sequence, specific ultraviolet (UV) absorbance at a wavelength of 254nm (SUVA254), and several gas chromatography–mass spectrometry (GCMS) techniques. Each NMR spectrum was analyzed by either calculating the total signal intensity found within defined chemical shift regions or by using the distribution of signal intensity across the entire spectrum. Relative to the traditional use of spectral regions analyzing the whole spectrum allowed variations in the size of individual peaks and their chemical shift values to be incorporated into the analysis. DP and CP 13C NMR analyses revealed differences in both the chemical shift regions and the spectra between forest floor OM and WEOM. Forest floor OM was relatively enriched in aromatic, phenolic, and di-O-alkyl regions, while WEOM showed relatively stronger signals in the carbonyl, alkyl, methoxyl, and O-alkyl regions. Harvesting had a negligible impact, with comparable spectra obtained for reference and harvested lodgepole (Pinus Contorta) samples. However, fire-affected samples displayed differences in aromatic and phenolic regions, consistent with increased presence of charred materials. Spectral differences were also observed between conifer and trembling aspen (Populus Tremuloides), with conifer samples enriched in alkyl and O-alkyl C compared to aspen samples. The 13C NMR analysis was complemented by SUVA254, where the SUVA254 values were consistent with the aromatic character that 13C NMR measured. Different GCMS analytical techniques were evaluated, including thermal desorption and pyrolysis along with the use of tetramethylammonium hydroxide to identify the most effective approach for generating a comprehensive WEOM chemical fingerprint. Overall, no single GCMS technique outperformed any of the others, but method selection should depend on specific study objectives. The most sensitive method for distinguishing differences between forest floors and mineral soil horizons was thermal desorption phase gas chromatography–mass spectrometry (TDGCMS) with the addition of tetramethylammonium hydroxide (TMAH). For identifying differences among dominant canopy vegetation types, pyrolysis phase gas chromatography–mass spectrometry (PyGCMS) was the best suited analysis and double-shot PyGCMS was most effective in capturing chemical signatures related to fire impact. Forest floor WEOM compositions differed from those of underlying mineral horizons, with forest floor WEOM relatively enriched in carbohydrates and phenolic compounds, while mineral soil WEOM contained proportionally more nitrogen-containing, aromatic, and aliphatic compounds. Clear-cut harvesting had a negligible impact on WEOM, with the chemical signature of both forest floor and mineral soil under disturbance comparable to that of reference sites, suggesting the enduring influence of dominant canopy vegetation. However, fire disturbance impacted forest floor WEOM signatures more significantly than the mineral soil WEOM, where the samples were associated with higher aromaticity and black carbon biomarkers were detected. Overall, the combination of 13C NMR, SUVA254 and GCMS has facilitated a thorough assessment of the chemistry of forest floor C and the associated WEOM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".