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Record W6921874676 · doi:10.7939/r3-0a1p-5z06

Beyond mountain pine beetle: soil carbon storage a decade after tree mortality and the possible influence of soil fungi

2024· dissertation· en· W6921874676 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2024
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSoil carbonSoil organic matterEcological successionEcosystemSoil textureCarbon cycleEctomycorrhizaSoil waterTaigaSoil fertility

Abstract

fetched live from OpenAlex

Mountain pine beetle (MPB; Dendroctonus ponderosae) disturbances, amplified by climate change, have led to extensive tree mortality and ecosystem succession in boreal forests across western Canada. Often following attack, former ectomycorrhizal (EM) pine stands in Alberta are replaced by arbuscular (AM) mycorrhizal shrubs and forbs. There has been growing interest in the varying ways that EM versus AM fungi influence critical soil processes, particularly concerning carbon cycling. While our comprehension of the specific impacts that MPB-induced mycorrhizal community succession has on long-term soil carbon storage remains somewhat limited, emerging frameworks offer broad yet valuable initial insights into this complex dynamic. Specifically, given the functional differences between mycorrhizal fungal types, a shift from EM- to AM-dominance in affected stands may promote soil organic matter formation and carbon stabilization, potentially enhancing soil carbon stores. The objective of this study was to address the following questions: 1) Will the dominant mycorrhizal type change with disturbance? 2) Does extensive tree mortality change the amount of carbon and nitrogen stored in forest floors and mineral soils? I sampled soil and conducted vegetation surveys at 80 lodgepole pine-dominated sites across west-central Alberta, split by disturbance severity (forests with >70% lodgepole pine basal area killed by MPB versus intact forest) and soil texture (coarse versus fine). I used density fractionation to separate soils into mineral-associated organic matter (MAOM) and particulate organic matter (POM) pools. I explored whether disturbance, mycorrhizal type, soil texture, and sampling depth affected carbon and nitrogen concentrations in these pools. There was a pronounced shift in mycorrhizal community composition from EM to AM dominance following MPB- induced forest transformations. However, overall carbon and nitrogen concentrations in bulk mineral soil, and carbon and nitrogen stocks in forest floors, remained largely unaffected. In addition, I confirmed that carbon and nitrogen storage are strongly dependent on soil texture and depth. Though there were no changes in soil carbon concentrations in the bulk soil, carbon in the MAOM fraction was relatively higher in the disturbed than intact sites. Greater carbon and nitrogen allocation into the MAOM pools is consistent with emerging hypotheses on the importance of AM vegetation to long-term carbon storage in soils. Taken together, these patterns in carbon storage may point to the resiliency of these soils, supported by soil organic matter interactions with the mineral matrix and potentially the buffering effects from the increase of AM fungi and their unique nutrient dynamics. The findings from this study may enhance our understanding of soil processes, offering opportunities to optimize soil carbon stocks and ecosystem services in recovering boreal forests.

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 score0.229
Threshold uncertainty score0.455

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 head0.005
GPT teacher head0.183
Teacher spread0.178 · 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
Published2024
Admission routes1
Has abstractyes

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