Vegetation type influences particulate organic matter storage along a low Arctic vegetation gradient
Bibliographic record
Abstract
Abstract Permafrost soils constitute a large part of the terrestrial carbon pool that is vulnerable to future climate warming. Continued warming of the low Arctic is also leading to the encroachment of large shrubs and trees into tundra ecosystems with effects on microbial community composition, organic matter cycling and physical soil parameters. To date it is still largely unknown how such vegetation shifts affect soil organic matter cycling in permafrost soils on short and long timescales. Here, we investigated differences in soil organic matter properties under graminoid tussock ( Eriophorum vaginatum ), birch shrub ( Betula glandulosa ), spruce tree ( Picea mariana ) and alder shrub ( Alnus viridis ) vegetation by density fractionation and subsequent measurements of organic carbon, total nitrogen, δ 13 C, and lignin phenol biomarker contents. Particulate organic matter constituted 1.3–11.3% of soil weight and stored between 29 and 89% of the total soil lignin, 12–60% of organic carbon and 6–40% of total nitrogen. The contribution of particulate organic matter generally decreased with soil depth. Soils under Alnus viridis showed significantly higher amounts of particulate organic matter and stored more lignin, organic carbon and total nitrogen in particulate form in all soil depths. Sites dominated by Eriophorum vaginatum exhibited higher lignin content and lower degradation state in the subsoil, which was associated with water saturation and low active layer depth. We conclude that the effect of vegetation changes on soil organic matter cycling is dependent on plant species with the encroachment of Alnus viridis shrubs potentially increasing the deposition of particulate organic matter into permafrost soils.
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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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".