Geographic variability in freshwater methane hydrogen isotope ratios and its implications for global isotopic source signatures
Bibliographic record
Abstract
There is growing interest in developing spatially resolved methane (CH 4 ) isotopic source signatures to aid in geographic source attribution of CH 4 emissions. CH 4 hydrogen isotope measurements ( δ 2 H–CH 4 ) have the potential to be a powerful tool for geographic differentiation of CH 4 emissions from freshwater environments, as well as other microbial sources. This is because microbial δ 2 H–CH 4 values are partially dependent on the δ 2 H of environmental water ( δ 2 H–H 2 O), which exhibits large and well-characterized spatial variability globally. We have refined the existing global relationship between δ 2 H–CH 4 and δ 2 H–H 2 O by compiling a more extensive global dataset of δ 2 H–CH 4 from freshwater environments, including wetlands, inland waters, and rice paddies, comprising a total of 129 different sites, and compared these with measurements and estimates of δ 2 H–H 2 O, as well as δ 13 C-CH 4 and δ 13 C–CO 2 measurements. We found that estimates of δ 2 H–H 2 O explain approximately 42 % of the observed variation in δ 2 H–CH 4 , with a flatter slope than observed in previous studies. The inferred global δ 2 H–CH 4 vs. δ 2 H–H 2 O regression relationship is not sensitive to using either modelled precipitation δ 2 H or measured δ 2 H–H 2 O as the predictor variable. The slope of the global freshwater relationship between δ 2 H–CH 4 and δ 2 H–H 2 O is similar to observations from incubation experiments but is different from pure culture experiments. This result is consistent with previous suggestions that variation in the δ 2 H of acetate, controlled by environmental δ 2 H–H 2 O, is important in determining variation in δ 2 H–CH 4 . The relationship between δ 2 H–CH 4 and δ 2 H–H 2 O leads to significant differences in the distribution of freshwater δ 2 H–CH 4 between the northern high latitudes (60–90 ∘ N), relative to other global regions. We estimate a flux-weighted global freshwater δ 2 H–CH 4 of −310 ± 15 ‰, which is higher than most previous estimates. Comparison with δ 13 C measurements of both CH 4 and CO 2 implies that residual δ 2 H–CH 4 variation is the result of complex interactions between CH 4 oxidation, variation in the dominant pathway of methanogenesis, and potentially other biogeochemical variables. We observe a significantly greater distribution of δ 2 H–CH 4 values, corrected for δ 2 H–H 2 O, in inland waters relative to wetlands, and suggest this difference is caused by more prevalent CH 4 oxidation in inland waters. We used the expanded freshwater CH 4 isotopic dataset to calculate a bottom-up estimate of global source δ 2 H–CH 4 and δ 13 C-CH 4 that includes spatially resolved isotopic signatures for freshwater CH 4 sources. Our bottom-up global source δ 2 H–CH 4 estimate (−278 ± 15 ‰) is higher than a previous estimate using a similar approach, as a result of the more enriched global freshwater δ 2 H–CH 4 signature derived from our dataset. However, it is in agreement with top-down estimates of global source δ 2 H–CH 4 based on atmospheric measurements and estimated atmospheric sink fractionations. In contrast our bottom-up global source δ 13 C-CH 4 estimate is lower than top-down estimates, partly as a result of a lack of δ 13 C-CH 4 data from C 4 -plant-dominated ecosystems. In general, we find there is a particular need for more data to constrain isotopic signatures for low-latitude microbial CH
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".