A cross-site comparison of ecosystem- and plot-scale methane fluxes from wetlands and uplands
Bibliographic record
Abstract
Abstract. Wetland and upland ecosystems play significant but opposing roles in the global methane (CH4) budget, acting as natural sources and sinks, respectively. Two of the most common approaches for measuring CH4 fluxes (FCH4) are chambers, which capture temporally intermittent, fine-scale spatial heterogeneity (ca. 1 m2), and eddy covariance (EC) towers, which cover a larger area (ca. 100–10000 m2) at a longer term. Although chamber and EC observations have been combined in various syntheses and databases to estimate CH4 budgets, a unified cross-site evaluation of FCH4 estimates at plot and ecosystem scales is lacking. As a first step toward a systematic spatiotemporal scaling of EC tower and chamber footprints, we quantified the differences between site-level aggregate FCH4 (EC vs chamber; ΔFCH4) from ten wetland and upland sites at half-hourly, hourly, daily, weekly, monthly, and annual timescales. We found that ecosystem-scale median FCH4 was consistently higher than plot-scale FCH4 at all temporal scales, with the smallest difference at daily timescale (multi-site median ΔFCH4: 1.36 nmol m-2 s-1; ~ 104 % higher ecosystem-scale than plot-scale FCH4) and largest at annual scales (2.58 nmol m-2 s-1; ~ 87 % higher ecosystem-scale than plot-scale FCH4). In general, the agreement between ecosystem- and plot-scale FCH4 decreased with finer temporal resolution (from Spearman ⍴ = 0.95 at annual scale to ⍴ = 0.65 at half-hourly scale), while ΔFCH4 variation was greatest at daily-to-annual scales. Key environmental predictors of ΔFCH4 included plot-scale spatial heterogeneity, dominant vegetation type, vapor pressure deficit, atmospheric pressure, and friction velocity at the daily and monthly scales. Wind direction was a significant predictor only at the monthly scale, suggesting EC footprint effects. These findings suggest accounting for variation in EC footprint extent, chamber measurement placement and artifacts is key to reconciling multi-scale FCH4 observations in diverse ecosystems and refining CH4 budgets.
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 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 | 0.000 | 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".