Organic matter quality in cryosols : effect on soil nitrogen dynamics and greenhouse gas emissions
Bibliographic record
Abstract
Over the past millennia, complex terrestrial ecosystems have evolved in the Arctic. However, the stability of these unique ecosystems is in jeopardy because of climate changes. Due to the fact that Arctic soils store great amounts of carbon (C) in soil organic matter (SOM), any change that may occur in SOM with climate changes may substantially affect many aspects of Arctic ecosystems such as vegetation, animals, and humans. On a more global perspective, any change in Arctic SOM has the potential of modifying the overall world climate by affecting the global greenhouse gas (GHG) budget. A better understanding of the soil factors that affect soil N and C cycling at the landscape scale, such as moisture, temperature, and SOM characteristics, is necessary to produce better models. The overall objective of this study was to characterize the properties of SOM in Arctic soils and their influence on soil N and C cycling dynamics - including GHG emissions - at the landscape scale. This study was conducted in three distinct Arctic ecosystems: Sub-Arctic (Churchill, MB), Low-Arctic (Daring Lake, NWT), and High-Arctic (Truelove, NU). For each site, the sampling locations were evenly divided into five landform units: 1) upper slope (Up), 2) back slope (Back), and 3) lower slope (Low) for catena sites, and 4) hummock (Hum) and 5) wedges of hummock (W) for hummocky sites (i.e., hummock in Churchill and ice-wedge polygons in Truelove). All sites were sampled at the end of their growing season (from 2 to 3 weeks before plant senescence). The characteristics of SOM were assessed using three methods: 1) density fractionation to separate the uncomplexed light fraction (LF) from heavy fraction (HF) of SOM (LF < 1.55 g mL⁻¹ < HF), 2) solid-state CPMAS ¹³C nuclear magnetic resonance (NMR) spectroscopy that determined the relative proportions of carbonyl-C (CbyC), alkyl-C (AC), aromatic-C (AroC), o-alkyl-C (OAC), and carbohydrates-C (CC), and 3) water-extractable organic matter (WEOM) that estimated SOM diluted in soil solution. Soil gross N mineralization was measured in situ using ¹⁵N dilution technique. Soil GHG emissions [nitrous oxide (N₂O), methane (CH₄), and carbon dioxide (CO₂)] were measured in situ using a multicomponent Fourier transform infrared gas analyzer coupled with an automated dark chamber. The first study showed that organic surface soils, which had more than 17% soil organic C (SOC) by weight, contained relatively more labile SOM than mineral surface soils (< 17% SOC). For example, OAC:AroC ratios of the organic soils ranged from 25 to 75% greater compared to mineral soils. At Churchill, Daring Lake, and Truelove, 53, 73, and 20% of the C and N was included in the LF, respectively. All results show that the organic soils of Sub- and Low-Arctic ecosystems sampled for this study contain more fresh and un-decomposed plant residues than High-Arctic organic soils. The second study showed that both topography and ecosystems had a significant impact on gross N mineralization and CO₂ emission rates. For example, at Churchill, gross N mineralization increased about 6-fold from upper slope to lower slope areas. Similarly, at Daring Lake, CO₂ emissions increase about 5-fold from upper slope to lower slope areas. Topography and ecosystems had a very limited impact on soil N₂O and CH₄ emissions most likely because net emissions were extremely low. The third study showed that soil moisture, SOM quantity, and labile SOM parameters such as OAC:AroC and water-soluble organic carbon (WSOC) positively influenced gross N mineralization, N₂O, and CO₂ emissions, whereas the relative proportion of AroC negatively influenced gross N mineralization, N₂O, and CO₂ emissions. Relationships between SOM characteristics and CH₄ emissions were not significant. This study showed that Up and Back areas tended to store relatively more recalcitrant SOM (AroC) than Low, Hum, and W areas, suggesting less fresh plant input on these landform units. Assessing SOM qualities with the ability of the soils to mineralize N (i.e., gross N mineralization) and release GHG at the landscape scale and across the Arctic represents a great advance in the understanding of these complex and unique ecosystems. Lower proportion of fresh and labile SOM found on Up and some Back landform units compared to Low and hummocky sites suggest that plants have more difficulties establishing and growing on these landform units (e.g., Up and Back) that experience harsh climates. Therefore, generalizations of the climate change impacts on soil N and C cycling processes throughout Arctic landscapes and ecosystems are less certain if topography is not considered. These results are particularly important because they can be used to produce better models that evaluate SOM stocks and dynamics under several climate scenarios and across Arctic landscapes and ecosystems.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".