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Record W3171007748 · doi:10.5194/egusphere-egu21-13734

Microbial community composition is linked to Sphagnum acclimation to warming

2021· article· en· W3171007748 on OpenAlexaff
Tatjana Živković, Alyssa A. Carell, Gustaf Granath, Mats B. Nilsson, Manuel Helbig, Denis Warshan, Ingeborg J. Klarenberg, Daniel Gilbert, A. Jonathan Shaw, Joel E. Kostka, David J. Weston

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsDalhousie UniversityMcGill University
Fundersnot available
KeywordsSphagnumAcclimatizationNutrientPeatBiologyAnimal scienceEcologyChemistryBotany

Abstract

fetched live from OpenAlex

Peatlands store about third of the terrestrial carbon (C) and exert long-term climate cooling. Dominant plant genera in acidic peatlands, Sphagnum mosses, are main contributors to net primary productivity. Through associative relationships with diverse microbial organisms (microbiome), Sphagnum mosses control major biogeochemical processes, namely uptake, storage and potential release of carbon and nitrogen. Climate warming is expected to negatively impact C accumulation in peatlands and alter nutrient cycling, however Sphagnum-dominated peatland resilience to climate warming may depend on Sphagnum-microbiome associations. The ability of the microbiome to rapidly acclimatize to warming may aid Sphagnum exposed to elevated temperatures through host-microbiome acquired thermotolerance. We investigated the role of the microbiome on Sphagnum’s ability to acclimate to elevated temperatures using a microbiome-transfer approach to test: a) whether the thermal origin of the microbiome influences acclimation of Sphagnum growth and b) if microbial benefits to Sphagnum growth depend on donor Sphagnum species. Using a full-factorial design, microbiomes were separated from Sphagnum “donor” species from four different peatlands across a wide range of thermal environments (11.4-27°C). The microbiomes were transferred onto germ-free “recipient” Sphagnum species in the laboratory and exposed to a range of experimental temperatures (8.5 – 26.5°C) for growth analysis over 4 weeks. Normalized growth rates were maximized for plants that received a microbiome from a matched “donor” and with a similar origin temperature (ΔTtreatment-origin: 0.3±0.9°C [±standard error], p = 0.73). For non-matched “donor-recipient” Sphagnum pairs, ΔTtreatment-origin was slightly negative with -4.1±2.1°C (p = 0.06). The largest growth rate of the “recipient” was measured when grown with a microbiome from a matching “donor” Sphagnum species and was 252% and 48% larger than the maximum growth rate of the germ-free Sphagnum and the non-matched “donor-recipient” Sphagnum pairs, respectively. Our results suggest that the composition of the Sphagnum microbiome plays a critical role in host plant temperature acclimation. We found that microbially-provided benefits to the host plant were most pronounced when: 1) the thermal origin of the microbiome is similar to experimental temperatures, and 2) when donor and recipient Sphagnum species are the same. Together, these results suggest that Sphagnum temperature acclimation can be modulated, in part, by microbial interactions and may potentially play a role in peatland resilience to climate warming.

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)0.001
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.256
Teacher spread0.238 · 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
Published2021
Admission routes1
Has abstractyes

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