MétaCan
Menu
← Back to cohort
Record W3179914357 · doi:10.5194/egusphere-egu22-6825

Biogeochemical and Ecological Responses to Warming Climate in High Arctic Polar Deserts

2022· preprint· en· W3179914357 on OpenAlexaffabout
Mitsuaki Ota, Amanda L. Muller, Gurbir Dhilon, Steven D. Siciliano

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPermafrostBiogeochemical cycleDiapirArcticEnvironmental scienceGreenhouse gasNutrientBiomass (ecology)Earth scienceEcologyGeologyOceanographyGeomorphologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.276
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

Same topicClimate change and permafrost→French-language works237,207→