Spatial heterogeneity of soil organic matter and microbial community composition across ice-wedge polygons and soil layers in Arctic lowland tundra
Bibliographic record
Abstract
Abstract. Arctic lowland tundra is characterized by pronounced spatial heterogeneity that introduces uncertainty into predictions of permafrost soil carbon dynamics. In these ecosystems, edaphic variability is primarily structured along two spatial axes: ice wedge polygon microtopography at the terrain scale and soil layers at the pedon scale. Here, we investigated how polygon types (low-, flat-, and high-centered polygons) and major soil layers (organic topsoil, mineral subsoil, cryoturbated material, and upper permafrost) jointly shape soil organic matter pools, microbial community composition, and potential extracellular enzyme activities. Polygon-specific patterns in soil organic matter characteristics and microbial communities persisted across all soil layers, and soil-layer specific differences were consistent across polygon types, while interactive effects were comparatively minor. Low centered polygons showed reduced organic matter bioavailability, lower microbial abundances, and diminished hydrolytic enzyme potential compared to flat- and high-centered polygons. Organic topsoils emerged as pronounced microbial and enzymatic hotspots. The upper permafrost contained substantial amounts of relatively undecomposed organic matter and indicated a considerable potential for hydrolytic degradation upon thaw. Across both spatial axes, patterns in soil organic matter pools, and microbial communities were largely structured along gradients in organic matter inputs and redox conditions, which themselves arise from interactions in surface microtopography, hydrology, and vegetation. Overall, our findings demonstrate that a limited number of spatial units captures a disproportionate share of edaphic, microbial, and biogeochemical variability in Arctic lowland tundra soils. Explicitly accounting for polygon morphologies and major soil layers therefore provides a tractable framework for upscaling soil processes across spatially heterogeneous ecosystems and improving climate-relevant biogeochemical projections.
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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 | 0.001 |
| 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".