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Record W7162123567 · doi:10.82308/39396

Modeling microbial dynamics and nutrient cycles in ombrotrophic peatlands

2022· dissertation· en· W7162123567 on OpenAlexaboutno aff
Siya Shao

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPeatOmbrotrophicBogBiogeochemical cycleNutrientEcosystemNutrient cycleMireWetlandCycling

Abstract

fetched live from OpenAlex

Peatlands store a vast amount of carbon (C) and have functioned as C sinks for millennia. The C sink function of peatlands may be at risk with increased nutrient deposition and climate change in the future. Models can make future projections for peatlands, but most peatland models do not include nutrient cycles, which are tightly couple to the C cycle. Furthermore, microbial activities have been found to play an essential role in regulating peatland biogeochemical cycles. Still, peatland models have not explicitly included any microbial controls, precluding our ability to examine the microbial feedbacks within the peatland ecosystem. This research explores the significance of microbe-mediated carbon-nutrient cycling in peatland ecosystem functions through a modelling approach. The McGill Wetland Model (MWM) was modified into MWMmic_NP by introducing a multi-layer cohort model to track the decrease in peat qualities with decomposition age; the growth and metabolism of saprotrophic microbes (SAP) as factors on the rates of peat decomposition; nitrogen and phosphorus cycles regulating the growth of plants and microbes; ericoid mycorrhiza fungi (ERM) exchanging nutrients for C from the host plant ericaceous shrubs; and the vertical and horizontal transport of the solutes in the peat pore water. MWMmic_NP was evaluated against the extensive whole-ecosystem measurements from the Mer Bleue Bog, eastern Canada, and the long-term fertilization experiments at the same site. MWMmic_NP was then used to examine the response of the bog to different scenarios of environmental changes. MWMmic_NP was able to replicate the overall C-N-P cycles observed at the Mer Bleue bog. In particular, the model reproduced the observed dynamics of the newly added pools of SAP and dissolved organic matter, and captured the changes in stoichiometry profiles with peat depth. Furthermore, the model performed well in reproducing the response of the bog to nutrient additions. A diminished role of mycorrhiza fungi in nutrient uptake and subsequent lower C allocation from shrubs to ERM increased shrub growth. Possible environmental changes induced a transition from mosses to shrubs domination in the vegetation community and from ERM to SAP domination in microbial community composition in the bog, thus reducing carbon sequestration capacity. Water table drawdown and increased soil temperature were the most important environmental factors for the weakening of the C sink. Reactions of SAP or ERM to changes in environmental conditions determined the response of the bog. This research contributes to a better understanding of the significance of microbe-mediated biogeochemical cycling in peatlands. ERM fungi may play a central role in maintaining the vegetation structure and C sink function of shrub-dominated ombrotrophic peatlands

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.326

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 science0.0010.000
Research integrity0.0010.000
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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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