Biogeochemical and Ecological Responses to Warming Climate in High Arctic Polar Deserts
Bibliographic record
Abstract
High Arctic polar deserts cover 26% of the Arctic and are predicted to transform dramatically with rapidly rising temperatures. Previous studies found that polar deserts store larger amounts of soil organic carbon (SOC) in the permafrost than previously expected and can emit greenhouse gases (GHGs) at rate comparable to mesic Arctic ecosystems. However, the mechanism of the GHG production is not clear, which contributes to a great source of uncertainty regarding ecological feedbacks to the warming climate. Extreme climate conditions thaw the uppermost part of the permafrost, and the accumulated soil nutrients are ejected into the overlying soil layers where the subsurface nutrient patches (diapirs) form to increase carbon and nitrogen (N) contents by 7% and 20%, respectively. Previous mechanical models suggest that the ejection is facilitated by the increase in soil viscosity in the overlying soil layer. We previously found that diapirs developed about 30% of sorted circles in our study site and that the dominant vascular plant (Salix arctica) increased root biomass and nitrogen uptake from diapirs. To understand a GHG-feedback to the warming climate, we collected 40 soil samples with diapirs and 40 without diapirs during July and August 2013 to investigate gross N transformation rates and GHG emissions associated with diapirs in laboratory. Our study site encompasses two Canadian High Arctic polar deserts and is located near Alexandra Fjord (78°51′N, 75°54′W), Ellesmere Island, Nunavut, Canada. To deal with small amounts of nitrous oxide (N2O) emissions near or below the detection limit, we employed the hurdle models including (1) a Bernoulli component that models whether the data cross the detection limit based on covariates and (2) generalized linear model component that models the data above the detection limit. Our results showed that diapirs decreased gross N mineralization up to 48% and slowed carbon dioxide and methane emissions. Consistently, we found that diapirs contained more recalcitrant SOC using attenuated total reflectance Fourier transformed mid-infrared (ATR-FTIR) spectroscopy. ATR-FTIR also showed higher amounts of polysaccharides known to raise soil viscosity. The hurdle model approach showed that diapirs increased the estimated N2O emissions by up to 49% under wet conditions and suggested that the increase links to the increase in the probability of N2O emissions. On the other hand, under dry conditions, the hurdle models suggested that the increase in the estimated N2O emissions from diapirs links to the increase in the magnitude of the N2O emissions. The higher abundance of polysaccharides and recalcitrant SOC may indicate that biological factors are involved in forming diapirs and that diapirs supply vascular plants with nutrients as a result of a mutualistic relationship. Our study showed that diapirs altered GHG emissions and suggest that future research should include plant-microbe relationship in diapirs and other factors such as occlusion in soil aggregates for a more robust evaluation of diaper-GHG production. Furthermore, we suggest that the hurdle model may be a useful tool for evaluating N2O emissions that are locally small but could be critical in total in the Arctic.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".