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Record W4413753139 · doi:10.1088/1748-9326/adff98

Responding to rising waters and temperatures: greenhouse gas flux from a high-latitude coastal wetland

2025· article· en· W4413753139 on OpenAlexaboutno aff
Katharine C. Kelsey, A. Joshua Leffler, Matteo Petit Bon, Karen H. Beard

Bibliographic record

VenueEnvironmental Research Letters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceWetlandGreenhouse gasLatitudeFlux (metallurgy)Atmospheric sciencesClimatologyOceanographyGeologyEcology

Abstract

fetched live from OpenAlex

Abstract Climate change is exposing coastal landscapes to more flooding, in addition to rapidly rising temperatures. These changes are critical in the Arctic where the effects of sea level rise are exacerbated by the loss of sea ice protecting coasts, subsidence as permafrost thaws, and a projected increase in storms. Such changes will likely alter the land-atmosphere gas exchange of high-latitude coastal ecosystems, but the effects of flooding with warming remain unexplored. In this work we use a field experiment to examine the interacting effects of increased tidal flooding and warming on land-atmosphere CO2 and CH4 exchange in the coastal Yukon–Kuskokwim Delta, a large sub-Arctic wetland and tundra complex in western Alaska. We inundated dammed plots to simulate two levels of future flooding: low-intensity flooding represented by one day of flooding per summer-month (June, July and August), and high-intensity flooding represented by three-consecutive days of flooding per summer-month, crossed with a warming treatment of 1.4 °C. We found that both flooding and warming influenced greenhouse gas (GHG) exchange. Low-intensity flooding reduced net CO2 uptake by 20% (0.78 µmol m−2 s−1) regardless of temperature, and marginally increased CH4 emissions 0.83 nmol m−2 s−1 (33%) under ambient temperature, while decreasing CH4 emissions by −1.96 nmol m−2 s−1 (40%) under warming. In contrast, high-intensity flooding restored net CO2 uptake to control levels due to enhanced primary productivity under both temperature treatments. High-intensity flooding decreased CH4 emissions under ambient temperature by 0.76 nmol m−2 s−1 (30%), but greatly increased emissions under warming by 4.68 nmol m−2 s−1 (265%), presumably driven by increased plant-mediated CH4 transport. These findings reveal that GHG exchange responds rapidly and non-linearly to intensifying flooding, and highlight the importance of short-term flooding dynamics and warming in shaping future carbon cycling in this Arctic coastal wetland.

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 score0.019
Threshold uncertainty score0.039

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 communication0.0000.000
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 head0.011
GPT teacher head0.269
Teacher spread0.257 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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