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. Permafrost soils are highly vulnerable to climate change. Yet, carbon-flux forecasts for ice-wedge polygon tundra ecosystems remain uncertain due to pronounced spatial heterogeneity at both terrain and pedon scales. In this study, we investigated how soil organic matter pools, microbial community structure, and potential enzymatic activities vary across two spatial dimensions: polygon geomorphology (low-, flat-, and high-centered polygons) and soil layers (organic topsoil, mineral subsoil, cryoturbated material, and upper permafrost). Polygon-specific signatures of SOM and microbial profiles persisted across all layers, and layer- specific effects were consistent across polygon morphologies. Low-centered polygons differed markedly from the other polygon types, exhibiting lower bioavailability of organic matter, smaller microbial abundance, and reduced potential for hydrolytic degradation. Organic topsoils were most distinct from mineral subsoils in their SOM composition and from permafrost in their microbial community structure. They also functioned as microbial hotspots, showing the highest abundances and enzyme activities. Once thawed, permafrost SOM may also become rapidly mobilized due to its quantity, composition, and considerable potential for hydrolytic degradation. Taken together, our findings suggest that gradients in organic matter and redox conditions structured the variations found at both spatial scales. Anticipated polygon transitions, active-layer deepening, and abrupt thaw with climate change, are therefore likely to interactively accelerate soil carbon losses. We propose that distinguishing low-centered polygons from other polygon types, and organic topsoils from deeper soil layers, provides a tractable framework for scaling soil processes across the spatially heterogeneous Arctic lowland tundra.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".