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Record W6989741477

Carbon Stocks and Fluxes From a Boreal Conifer Swamp: Filling a Knowledge Gap for Understanding the Boreal C Cycle

2024· other· en· W6989741477 on OpenAlexfundaboutno aff

Bibliographic record

VenueEpsilon Open Archive (Sveriges lantbruksuniversitet biblioteket (Swedish University of Agricultural Sciences)) · 2024
Typeother
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaShell CanadaUniversity of WaterlooSvenska Forskningsrådet FormasSuncor Energy Incorporated
KeywordsUnderstoryBorealTaigaWetlandSwampBlack spruceCarbon cycleGrowing seasonPeatEcosystem
DOInot available

Abstract

fetched live from OpenAlex

The carbon (C) dynamics of boreal coniferous swamps are a largely understudied component of wetland carbon cycling. We investigated the above- and below-ground carbon stocks and growing season carbon dioxide (CO2) and methane (CH4) fluxes from a representative boreal coniferous swamp in northern Alberta, Canada in 2022. Tree inventories, understory vegetation biomass and peat cores were collected across three sub-sites within the broader swamp, with gas flux collars placed in the dominant plant communities present. Alongside the C flux measurements, environmental variables such as water table depth, soil temperature and growing season understory green leaf phenology were measured. Our results show that these boreal coniferous swamps store large volumes of organic C in their biomass and soil (134 kg C m-2), comparable with other wetland and forest types, although 95% of the total C stock at our site was within the soil organic carbon. We also found that understory CO2 and CH4 fluxes indicated that the ground layer of the site is a source of greenhouse gases (GHGs) to the atmosphere across the growing season. However, we did not measure litterfall input, tree GHG fluxes or net primary productivity of the overstory, therefore we are not able to say whether the site is an overall source of C to the atmosphere. This study provides a much-needed insight into the C dynamics of these under-valued wetland ecosystems, and we highlight the need for a coordinated effort across boreal regions to try to improve inventories of C stocks and fluxes.Compared to other wetland types across Canada, boreal conifer swamps do not receive the same level of scientific attention and therefore our understanding of how much carbon they potentially store and release is limited. To fill this knowledge gap, our study measured how much carbon was stored both in the trees and within the soil itself, alongside measurements of carbon uptake and release within a representative conifer swamp wetland in Western Canada. We found that although these wetlands may function similarly to other wetland types, by ignoring them, we are missing out on large amounts of carbon being stored in these systems. We also found that at the ground layer, these sites are a source of carbon, that is, releasing more carbon than is being taken up by the understory moss and plant layer. However, we cannot say if the site overall is a source of carbon to the atmosphere as we were unable to measure other key components of a wetland carbon cycle including litterfall input and the productivity of the trees themselves. Our findings indicate that by not including these wetlands in modeling of carbon dynamics, we are missing a substantial component of boreal carbon cycling processes.Boreal conifer swamp wetlands are an underrepresented wetland class within wetland C cycling measurements and modeling These swamps store large volumes of organic C in their aboveground biomass but most importantly, in their soil organic carbon stock We also found that the ground-layer of this site is a net source of greenhouse gases during the growing season

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 score0.627
Threshold uncertainty score0.750

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.248
Teacher spread0.218 · 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 routes2
Has abstractyes

Explore more

Same venueEpsilon Open Archive (Sveriges lantbruksuniversitet biblioteket (Swedish University of Agricultural Sciences))→Same topicPeatlands and Wetlands Ecology→French-language works237,207→