Global estimates of forest soil methane flux identify a temperate and tropical forest methane sink
Bibliographic record
Abstract
Forest ecosystems play an important role in the global CH4 cycle. Understanding and quantifying the contribution and distribution of CH4 sinks and sources in global forest soils is vital for assessing realistic approaches to climate change mitigation. Here, we compiled a dataset of in situ global forest soil CH4 fluxes from published data, incorporating 772 case studies covering boreal (n = 12), temperate (n = 369), subtropical (n = 208), and tropical (n = 183) forests and spanning 1991–2020 as a basis to build the mixed-effect model. Using the screened best model, we identified the main drivers and predicted the global distribution of the forest soil CH4 flux. Our research revealed that global forest soil CH4 uptake decreased significantly with increasing mean annual temperature (MAT), soil bulk density (BD), soil organic carbon (SOC), and soil total nitrogen (TN) but increased significantly with increasing mean annual precipitation (MAP). The global mean CH4 uptake rate in forest soils was 3.95 ± 1.78 kg CH4 ha−1 yr−1, with the total sink of 14.98 ± 6.75 Tg CH4 yr−1. The soil CH4 sinks in temperate and tropical forests contributed 84 % to the total sink of global forests. The CH4 emission rate in global forest soils averaged 1.12 ± 1.11 kg CH4 ha−1 yr−1, with the total source of 0.14 ± 0.14 Tg CH4 yr−1. Nearly 3 % of the total area of global forest soils was a net CH4 source. In summary, we identified the key drivers of forest soil CH4 flux and improved previous estimates of the global CH4 budget in forest soils. These findings can support decision-making related to forest management and greenhouse gas restrictions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".