Greenhouse Gas Fluxes Along a Trophic Gradient in Thermokarst Wetlands
Bibliographic record
Abstract
Northern peatlands in the discontinuous permafrost zone of Canada’s Taiga Plains store vast carbon stocks, but are increasingly vulnerable to permafrost thaw and climate change. Elevated peat plateaus are collapsing into nutrient-poor thermokarst bogs and into fens that range from nutrient poor to extreme-rich, fundamentally altering carbon dynamics. Greenhouse gas (GHG) fluxes across this trophic gradient remain poorly understood, despite fundamental differences in hydrology, vegetation, and nutrient status. This knowledge gap constrains predictions of carbon-climate feedbacks in rapidly changing northern landscapes. This study examined methane (CH4) and carbon dioxide (CO2) fluxes across a complete trophic gradient (bog, poor fen, rich fen, and extreme-rich fen) in the discontinuous permafrost zone near Lutose, Alberta, during two consecutive extreme drought years (2023-2024). Using chamber-based measurements from 30 collars, supported by water chemistry analyses, vegetation surveys, and process-based modeling over 16 field campaigns, this study provides one of the first year-round assessments of GHG dynamics across the trophic gradient in the Taiga Plains. Annual CH4 emissions (95 % CI) followed a non-linear pattern relative to trophic level: the bog emitted 14 [11, 17] in 2023 and 16 [13, 20] in 2024, the poor fen 27 [21, 36] and 14 [11, 17], the rich fen 42 [32, 55] and 30 [24, 37], and the extreme-rich fen 12 [9.2, 17] and 11 [8.6, 15] g C-CH4 m⁻2 yr⁻1. Suppression of methanogenesis in highly minerotrophic fens likely explains this pattern, challenging models that treat fen types as interchangeable. Winter fluxes accounted for 28 % (bog), 19 % (poor fen), 15 % (rich fen), and 6.9 % (extreme-rich fen) of annual totals, underscoring the importance of cold-season processes. CO2 dynamics revealed equally complex patterns. Gross primary production (GPP) and ecosystem respiration (ER) generally increased along the nutrient gradient, but net ecosystem exchange (NEE) varied unpredictably across sites. NEE indicated that the bog was a consistent sink, at -40 [-60, -22] in 2023 and -27 [-42, -10] g C-CO2 m⁻2 yr⁻1 in 2024. The poor fen was a strong source, at 360 [340, 370] and 280 [260, 300], while the rich fen remained a sink at -51 [-80, -22] and -44 [-66, -23]. The extreme-rich fen fluctuated between weak source and near-neutral, at 59 [38, 79] and 11 [-14, 39]. Vegetation functional groups strongly mediated fluxes, with sedges and forbs explaining large amounts of variation in GPP and ER. Process models revealed site-specific controls with temperature dominance in the bog, interactive drivers in intermediate fens, and unexpectedly simple relationships in the extreme-rich fen. Despite extreme drought conditions, the more nutrient-rich fens maintained water tables within ~10 cm of the surface, suggesting that hydrological connectivity buffered against moisture stress while sustaining methanogenesis even in dry years. These findings demonstrate that trophic level is a fundamental but non-linear regulator of peatland GHG dynamics. Models that collapse fen types obscure thresholds and feedbacks that may shift peatlands from sinks to sources under future change. Accounting for the difference in GHG balances as a result of trophic level is therefore essential to refine peatland carbon models and to improve predictions of their role in the global carbon cycle as climate warming and permafrost thaw accelerate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".