Hydrological and Temperature Controls on CO <sub>2</sub> and CH <sub>4</sub> Exchange Between a Mid-Altitude Mountain Peatland and the Atmosphere
Bibliographic record
Abstract
The aim of the present study is to understand the variability and the environmental factors controlling the fluxes of carbonaceous greenhouse gases (GHGs), methane (CH 4 ) and carbon dioxide (CO 2 ) between a temperate Sphagnum -dominated mid-altitude mountain peatland and the atmosphere. We conducted monthly measurements of GHG fluxes over 20 months using the chamber method. Specifically, we assessed the effects of (1) air temperature and (2) water level (WL) on GHG emissions. Open Top Chambers (OTC) were used to simulate a warming effect by passive heating of the air above the soil. To assess the effect of WL, we studied a hydrological gradient along a 35 m long transect from a near-surface “WET” area, through an “INTER” area with an intermediate WL, to a “DRY” area with a lower WL. The WET area featured a higher cover of Sphagnum species while the vegetation cover in the DRY area contained more vascular plants. Although all plots showed the same seasonality of GHG fluxes, considerable variability was observed among them. Raising the temperature using OTCs, which increased annual average air temperature by 0.2 °C to 0.6 °C, did not significantly affect CH 4 and CO 2 respiration (Reco) fluxes. In contrast, hydrological conditions played an important role in explaining flux variability. CH 4 fluxes were significantly higher in the WET and INTER areas (median [95 % CI] values: 17.5 [14.2, 29.0] and 20.0 [14.8, 30.4] nmol m -2 s -1 ) compared to the DRY area (3.4 [1.9, 9.4] nmol m -2 s -1 ) during all hydrological periods, i.e., humid spring, humid summer and dry summer. Reco did not vary significantly along the hydrological gradient overall, but the fluxes were lower in the WET area under humid spring (0.4 [0.3, 0.6] µmol m -2 s -1 ) and summer (1.7 [1.25, 2.75] µmol m -2 s -1 ) conditions compared to the DRY area (1.6 [1.3, 2.0] µmol m -2 s -1 in spring and 3.8 [2.7, 4.6] µmol m -2 s -1 in summer). Conversely, greater fluxes (by ~ 0.5 µmol m -2 s -1 ) were observed in the WET area during summer drought. Given that Reco emissions are expected to be higher during droughts in the DRY area, we hypothesise a possible threshold effect, such as inhibition of phenoloxidase activity and/or other enzymatic activities, which would limit organic matter decomposition. Moreover, increasing WL in the WET and INTER areas led to a drastic drop in gross primary production (GPP) corresponding to Sphagnum immersion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 teacher head, 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".