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How do microorganisms from permafrost soils respond to short-term warming?

2020· article· en· W3091934853 on OpenAlexaboutno aff
Victoria Martin, Julia Wagner, Niek Jesse Speetjens, Rachele Lodi, Julia Horak, Carolina Urbina-Malo, Moritz Mohrlok, Cornelia Rottensteiner, Willeke A' Campo, Luca Durstewitz, George Tanski, Michael Fritz, Hugues Lantuit, Gustaf Hugelius, Andreas Richter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostTundraSoil waterGlobal warmingIncubationAnimal scienceEnvironmental scienceArcticEcologyChemistryClimate changeEnvironmental chemistryBiologySoil scienceBiochemistry

Abstract

fetched live from OpenAlex

Arctic ecosystems outpace the global rate of temperature increases and are exceptionally susceptible to global warming. Concerns are raising that CO2 and CH4 released from thawing permafrost upon warming may induce a positive feedback to climate change. This is based on the assumption, that microbial activity increases with warming and does not acclimate over time. However, we lack a mechanistic understanding of carbon and nutrient fluxes including their spatial control in the very heterogeneous Arctic landscape. The objective of this study therefore was to elucidate the microbial controls over soil organic matter decomposition in different horizons of the active layer and upper permafrost. We investigated different landscape units (high-centre polygons, low-centre polygons and flat polygon tundra) in two small catchments that differ in glacial history, at the Yukon coast, Northwestern Canada. In total, 81 soil samples were subjected to short-term (eight weeks) incubation experiments at controlled temperature (4 °C and 14 °C) and moisture conditions. Heterotrophic respiration was assessed weekly, whereas physiological parameters of soil microbes and their temperature response (Q10) were determined at the end of the incubation period. Microbial growth was estimated by measuring the incorporation of 18O from labelled water into DNA and used to calculate microbial carbon use efficiencies (CUE). Microbial biomass was determined via chloroform fumigation extraction. Potential activities of extracellular enzymes involved in C, N, P and S cycling were measured using microplate fluorimetric assays. Cumulative heterotrophic respiration of investigated soil layers followed the pattern organic layers > upper frozen permafrost > cryoturbated material > mineral layers in both catchments. Microbial respiration responded strongly in all soils to warming in all soils, but the observed response was highest for organic layers and cryoturbated material at the beginning and end of the experiment. Average Q10 values at the beginning of the experiment varied between 1.7 to 4.3 with differences between horizons but converged towards Q10 values between 2.0min to 2.9max after eight weeks of incubation. Even though microbial biomass C did not change with warming, microbial mass specific growth was enhanced in organic, cryoturbated and permafrost soils. Overall, warming resulted in a 65% reduced CUE in organic horizons. Our results show no indication for physiological acclimatization of permafrost soil microbes when subjected to 8-weeks of experimental warming. Given that the duration of the season in which most horizons are unfrozen is rarely longer than 2 months, our results do not support an acclimation of microbial activity under natural conditions. Instead, our data supports the current view of a high potential for prolonged carbon losses from tundra soils with warming by enhanced microbial activity. This work is part of the EU H2020 project “Nunataryuk”.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.048
GPT teacher head0.241
Teacher spread0.193 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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