DOI: 10.5061/dryad.tb2rbp06v
title: Data from: Soil moisture threshold of methane uptake in alpine ecosystems

description: Methane (CH4) uptake in alpine ecosystems is an important component of the
 global CH4 sink. However, large uncertainties remain regarding the
 magnitude and spatial patterns of CH4 uptake, owing to its extensive
 spatial variability, diverse controlling factors, and limited
 regional-scale observations. Here, we investigated field ecosystem CH4
 uptake along a 3,200-km transect across various alpine grasslands on the
 Qinghai-Tibetan Plateau (QTP). We found a substantial spatial variation in
 in-situ CH4 uptake among alpine grasslands, with the highest rates in
 drier regions of the mid-western QTP. Soil moisture was the most important
 factor controlling CH4 uptake, exhibiting a remarkably low threshold of
 6.2 ± 0.1 v/v %. Below this threshold, CH4 uptake was constrained by soil
 moisture, moisture-induced nitrogen limitation, and high temperatures.
 Above this threshold, CH4 uptake was mainly limited by gas diffusion and
 low temperatures. By integrating grid predictors with a random forest
 model trained on 1,851 field measurements encompassing both our
 observations and a regional synthesis across the QTP, we estimated a
 regional CH4 uptake of 0.88 ± 0.020 Tg CH4 yr-1 from all alpine grasslands
 on the QTP. This higher estimate, primarily driven by alpine steppes, was
 significantly greater than current regional estimates from global CH4
 models. Our findings highlight the importance of the CH4 sink in dry
 alpine ecosystems characterized by low soil moisture, suggesting that the
 contribution of the CH4 sink in drylands may have been substantially
 underestimated in the current global CH4 budget.
description: <strong>Study area and sampling
 sites</strong> This study was conducted on the
 Qinghai-Tibetan Plateau (QTP), the highest and largest plateau on the
 Earth, with an average altitude of over 4,000 m above sea level and a
 broad area of 2.5 × 10<sup>6</sup>
 km<sup>2 .</sup> During July-August 2020, 58 sites were
 visited once across the QTP along a precipitation transect (mean annual
 precipitation (MAP): 56−712 mm yr <sup>-1</sup>, mean annual
 temperature (MAT): −7−5℃) of 3,200 km from northwest to southeast,
 covering all alpine grassland types. The grassland type at each site was
 defined according to the Chinese Vegetation map and through observations
 of the vegetation composition in the field.
 <strong>Field measurements and
 sampling</strong> To monitor
 <em>in-situ</em> CH<sub>4</sub> fluxes, we
 randomly selected three to six 1 m × 1 m quadrats within a 10 m × 10 m
 plot at each site. CH<sub>4</sub> fluxes were measured for 3−6
 replicates within each quadrat using an opaque chamber during the daytime
 between 9:00 a.m. and 12:00 a.m. Specifically, in each quadrat, a
 cylindrical polyvinyl chloride (PVC) collar (20 cm diameter, 10 cm height)
 was inserted 2 to 3 cm into the soil and connected to an opaque chamber
 (covering area: 276.27 cm², height: 44.5 cm). CH<sub>4</sub>
 concentrations in the chamber were measured over 3 minutes using a Los
 Gatos Research, Inc. (LGR) ultraportable greenhouse gas analyzer (MGGA,
 ABB, Canada) while the chamber was closed and formed a closed loop to the
 gas analyzer. A small fan was fixed inside the chamber to homogenize the
 inside air. Fluxes of CH<sub>4</sub> were computed by fitting
 a linear regression model to temporal changes in the chamber
 CH<sub>4</sub> concentration during the measurement period of
 3 minutes, with regression significance at <em>p</em> &lt;
 0.05, using the real-time measured chamber volume (which changed with the
 depth of the collar inserted into soils), air pressure, and temperature.
 In total, 1233 <em>in-situ</em> flux measurements were
 obtained in the field. An averaged flux was calculated for each quadrat,
 resulting in 290 CH<sub>4</sub> fluxes for further data
 analysis. Soil temperature (ST, °C) and volumetric soil moisture (SM, v/v
 %) at a depth of 0−7.5 cm were simultaneously recorded adjacent to the PVC
 collar for each CH<sub>4</sub> flux measurement using a TDR
 350 (FieldScout, Spectrum Technologies Inc., USA) with a 7.5 cm probe rod
 vertically inserted into the soil. To investigate
 vegetation characteristics, three out of the 3−6 quadrats within each site
 were randomly selected at all sites. Plant coverage (PC) and species were
 estimated in the field, followed by ground-level harvest of plant tissues.
 Plant roots were randomly collected from the same quadrats at depths of
 0−30 cm, where most roots are distributed. Three root cores (7.5 cm
 diameter) were taken and mixed in each quadrat to measure root biomass. In
 the laboratory, all plant samples (with roots washed beforehand) were
 oven-dried at 65°C for 48 hours and weighed to measure plant biomass (PB,
 which includes above-ground biomass and below-ground biomass at 0−30 cm
 soil depth). Plant richness (PR) was represented as the number of plant
 species in each quadrat. Plant coverage was estimated by the projection
 method. In the field, soil samples were collected from
 0−10 cm depth using a 7.5 cm diameter auger by randomly combining five
 individual soil cores at each of three selected quadrats. The mixed soil
 sample was then sieved with a 2-mm mesh after hand picking out rocks and
 roots and separated into two subsamples, one stored at −20°C for molecular
 analysis, and the other at 4°C for laboratory incubation and
 physiochemical analysis. Moreover, to measure soil bulk density (BD), an
 additional soil sample was collected from the top 10 cm soil depth at each
 of the three selected quadrats using a soil density ring (100
 cm<sup>3</sup>, 5 cm diameter).
 <strong>Laboratory incubation</strong>
 Following the field survey and sampling, soil samples for
 incubation were immediately transported to the laboratory and stored in
 4°C fridges. To assess soil CH<sub>4</sub> oxidation
 potential, 10 g of fresh soil for each soil sample was weighed into 150 mL
 glass bottles and pre-incubated for 7 days at 5°C with the field SM
 intact. After pre-incubation, all bottles were flushed with atmospheric
 air (<sub>1.9 ppm CH</sub>4<sub>) for 5 minutes, and
 sealed with rubber stoppers to create gas-tight conditions. At the
 beginning of the incubation, 30 ml of background atmospheric air was
 injected into each bottle to prevent under-pressure. Then, the soils were
 incubated in the dark at 5°C for an additional 48 hours, during which a 6
 ml gas sample was collected from the headspace every 12 hours using a
 gas-tight syringe. The CH</sub>4~ concentration for all gas samples
 was analyzed using a gas chromatograph equipped with a flame ionization
 detector (GC-7890B, Agilent, Santa Clara, CA, USA) within 24 hours after
 sampling. Gas fluxes were calculated using a second-order polynomial
 regression model to assess the changes in CH<sub>4</sub>
 concentration in the bottle over 48 hours, with the significance of the
 regressions evaluated at <em>p</em> &lt; 0.05. The slope
 at 10 hours of the fitted line was applied to calculate the
 CH<sub>4</sub> oxidation rate. Considering experimental costs,
 we selected 22 representative sites that cover all types of alpine
 grasslands, with each located approximately 150 km apart, for the
 laboratory incubation. **Soil parameters **
 Soil BD samples were oven-dried to a constant weight at 105°C,
 weighed, and then the ratio of oven-dried soil mass to mental ring volume
 (100 cm<sup>³</sup>) was calculated to represent BD. Soil
 texture (contents of clay, silt, and sand) was evaluated using a Malvern
 Mastersizer 2000 (Malvern, UK) after removing soil organic matter and
 carbonates using 35% H<sub>2</sub>O<sub>2</sub>
 and 10% HCl, respectively. Soil pH was measured in a 1:2.5 soil-water
 suspension using a pH meter (HM-30G, TOA Co., Japan). Contents of soil
 total carbon (TC) and total nitrogen (TN) were determined using a Vario EL
 cube elemental analyzer (Thermo Scientific, Bremen, Germany). Soil
 inorganic carbon (SIC) contents were quantified using a solid infrared
 carbon and sulfur analyzer (multi EA4000, Analytic-Jena, Germany). Soil
 organic carbon (SOC) was the difference between TC and SIC. Soil C/N was
 the ratio of SOC to TN. Soil available nitrogen (AN) was analyzed by the
 alkaline-hydrolysis diffusion method. Soil available phosphorus (AP) was
 determined by measuring phosphate using the ammonium molybdate
 spectrophotometric method following extraction with 0.03 M ammonium
 fluoride and 0.025 M hydrochloric acid. In addition,
 absolute abundances of methanotrophs were quantified by qPCR (quantitative
 Polymerase Chain Reaction) on the <em>pmoA</em> gene
 (particulate methane monooxygenase gene, the functional marker for
 detecting aerobic methanotrophs) using primer sets of A189f
 (5′-GGNGACTGGGACTTCTGG-3′) and mb661r (5′-CCGGMGCAACGT-CYTTACC-3′)
 according to the method used in our previous study. Again, to consider
 experimental costs, BD, soil texture, SIC, and <em>pmoA</em>
 abundance analysis were conducted only on soil samples of the selected 22
 representative sites. <strong>Regional synthesis
 and estimates from existing global
 CH<sub>4</sub></strong>
 <strong>models</strong> To obtain more data
 representing both temporal (diurnal and seasonal) and spatial variations
 of CH<sub>4</sub> uptake for model training and the estimation
 of regional CH<sub>4</sub> uptake, we conducted a regional
 synthesis of <em>in-situ</em> CH<sub>4</sub> flux
 across all uplands from QTP. We searched the Web of Science (http:// isiknowledge.com), Google
 Scholar (https://scholar.google.com), and the China National Knowledge Infrastructure (http://www.cnki.net) for published studies of CH<sub>4</sub> fluxes from alpine upland grasslands on the QTP, using the keywords ‘CH<sub>4</sub>’ or ‘methane’ and ‘Tibetan Plateau’. Data were included only when chamber-measured <em>in-situ</em> CH<sub>4</sub> flux and the simultaneously recorded SM and ST (0−10 cm soil depth) were all available. Overall, we compiled a database comprising 1,561 observations from 21 publications, distributed among 20 sites, encompassing in situ CH<sub>4</sub> uptake throughout different seasons from 2006 to 2021 (Supplementary Data). The regional synthesis dataset also considered the diurnal dynamics by including the daily-averaged <em>in-situ</em> CH<sub>4</sub> flux at the annual scale from an alpine meadow at Hongyuan station (32°48' N, 102°58' E), where the CH<sub>4</sub> flux was measured by auto-chambers with hourly measurement frequency. Combining the regional synthesis and our transect observations, the whole dataset exhibited ST ranging from −18.0°C to 45.1°C, effectively representing the temperature dynamics on the QTP. We also recorded corresponding information from the literature on vegetation types, location (longitude and latitude), MAP (mm), MAT (°C), AN (mg kg<sup>-1</sup>), and measurement dates. Furthermore, we retrieved existing regional CH<sub>4</sub> uptake estimates on the QTP from literature that evaluated regional CH<sub>4</sub> and global CH<sub>4</sub> uptake models.^^ For global CH<sub>4</sub> uptake models, global maps of CH<sub>4</sub> uptake were first clipped using the QTP boundary. The regional CH<sub>4</sub> uptake and CH<sub>4</sub> uptake for each vegetation type were then calculated using zonal statistics in QGIS (3.20.0-Odense), based on the vegetation map of QTP. <strong>Gridded climate, vegetation, and soil datasets</strong> Various remote-sensing datasets were gathered and analyzed using Google Earth Engine (GEE) to obtain climate (MAP and MAT) and plant data (Enhanced Vegetation Index (EVI)) for site information and regional predictions. To derive the climatic and plant information for each sampling location, we calculated MAT and MAP using ECMWF Reanalysis v5 (ERA5) air temperature and precipitation data from 1981 to 2020. We then reanalyzed the mean air temperature during the growing degree days (MTGDD3, defined as days with mean air temperature &gt; 3°C) and freezing degree days (MTFDD, defined as days with mean air temperature &lt; 0°C) using the ERA5 air temperature dataset for the same period. Using images with the date closest to the field observation date (0−7 days before or after the field observation date), we obtained EVI^^ for all field observations collected along our transect and from the literature, using a dataset of 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) EVI. To conduct the regional estimation of CH<sub>4</sub> uptake, datasets with high spatial and temporal resolution spanning a long timescale (2001−2021) were selected and reanalyzed as predictor datasets. Specifically, the SM input was chosen from a long-term, high temporal resolution dataset: surface SM with a spatial resolution of 0.25° (SMsurf, 7 cm soil depth) from the Global Land Evaporation Amsterdam Model (GLEAM). This dataset showed a good relationship with the <em>in-situ</em> surface SM from both our transect and regional synthesis. The ST input was a 1-km dataset from 2001 to 2021 converted from the 1-km MODIS Terra Land Surface Temperature (LST)^ ^using the linear relationship between our field-observed ST and LST-obtained surface temperature. For precipitation, we chose a 1-km monthly precipitation dataset for China. The EVI dataset was the 1-km MODIS EVI. The available nitrogen (AN) dataset was derived from a published China soil dataset of 1-km spatial resolution. Due to the varying time scales, all datasets were monthly resampled before being imported into the model. The SM dataset was further resampled to the 1-km resolution to ensure that all predictors were at the same spatial resolution, and units of each dataset were transformed to remain consistent with the observed dataset. <strong>Model predictions</strong> To estimate regional CH<sub>4</sub> uptake, 1-km grid CH<sub>4</sub> uptake rates on the QTP were calculated monthly using the random forest model and predictor datasets from 2001 to 2021. First, a random forest model was developed based on our transect data using the randomForest R package. Second, to avoid overfitting, random forest auto-selection was employed to simplify the model using the rfUtilities R package. Five variables, including climate, vegetation, and soil condition, were selected: MAP, EVI, ST, SM, and AN. Third, considering the geographical and temporal dynamics of CH<sub>4</sub> uptake across the QTP, we rebuilt the random forest model using the selected variables based on the database encompassing both our transect observations and the regional synthesis. The “leave one out” cross-validation was used to evaluate the prediction performance of the model. We calculated a bias as an average of the absolute error between predictions and actual observations, used Pearson correlation (<em>R</em>) to identify the strength of the linear relationship between observed and predicted fluxes, and calculated root mean squared error (RMSE) to estimate model errors. To assess potential prediction bias introduced by differences (e.g., slight variations in soil moisture measurement depths: 0−7.5 cm for transect vs 0−10 cm for regional synthesis) between our transect observations and the regional synthesis dataset, we compared the predicted CH<sub>4</sub> fluxes for our transect sites using the simplified model trained exclusively on our transect dataset and the rebuilt model trained on both our transect and the regional synthesis datasets. Finally, the rebuilt random forest model was applied to the predictor datasets, producing monthly step grid CH<sub>4</sub> uptake rates at 1-km resolution from 2001 to 2021. To quantify the uncertainty in our random forest model and the corresponding CH<sub>4</sub> uptake, we employed a repeated random resampling procedure. Through bootstrapping, we generated 200 datasets from the original flux data, each containing an identical number of observations. These 200 datasets were then utilized to produce 200 individual predictions using the random forest model, and the prediction uncertainty was quantified using the prediction interval (PI; 95th percentile–5th percentile). Subsequently, the uncertainty of the estimated annual CH<sub>4</sub> uptake was determined by calculating the range of annual CH<sub>4</sub> uptake across the 200 predictions. Using the monthly predictions, we then calculated the grid annual mean and total CH<sub>4</sub> uptake as well as the uncertainties for each year. Vegetation-type-based statistics were then derived using zonal statistics in QGIS (version 3.20.0-Odense), based on the vegetation map. Additionally, the differences between our estimation and current global CH<sub>4</sub> models for uplands on the QTP and for each vegetation type were assessed using the same vegetation map.
description: # Data from: Soil moisture threshold of methane uptake in alpine
 ecosystems
 [https://doi.org/10.5061/dryad.tb2rbp06v](https://doi.org/10.5061/dryad.tb2rbp06v) Here is the dataset used for the paper "Soil moisture threshold of methane uptake in alpine ecosystems" ## Description of the data and file structure The dataset includes information on all sampling sites, including climate, plant, soil and microbial properties. This dataset includes: 1\) Filed_and_lab_data.xlsx:Field and lab data of CH4 flux, climate, plant, soil,and microbial properties of each site along the transect; 2\) CH4_predictions_2001-2021_monthly.zip: CH4 prediction data at monthly step from 2001 to 2021; 3\) R code of Random Forest model for model building and regional predictions of CH4 flux. All the data were collected from 58 sites along a 3,200 km transect across various alpine grasslands on the Qinghai-Tibetan Plateau (QTP). The field survey and sampling took place during July and August 2020. For the excel file, the "all field data" sheet contains data measured in the field, while the "all lab data" sheet includes data from laboratory analyses. In sheet of "all field data": * Site ID: The unique identifier for each site along the transect, as referenced in the manuscript. - FieldSiteID_plot: The identifier for each site and plot (replicates for each site) used in the field study. * Positions: Replicates within the same plot. - MAP (Mean Annual Precipitation): The mean annual precipitation at each site. * Grassland_type: The type of alpine grassland at each site. - Soil Moisture and Soil Temperature: Measured at a depth of 0-7.5 cm in the field. * CH₄ Uptake: The in-situ ecosystem CH₄ uptake rate. In sheet of "all lab data": * MAT (Mean Annual Temperature): the mean annual temperature at each site. - MTGDD3: Mean air temperature during growing degree days (days with mean air temperature &gt; 3°C). * MTFDD: Mean air temperature during freezing degree days (days with mean air temperature &lt; 0°C). - PR (Plant Richness): The number of plant species. * PC (Plant Coverage): The percentage of ground covered by plants. - PB (Plant Biomass): Includes both aboveground biomass (AGB) and belowground biomass (BGB). * EVI (Enhanced Vegetation Index): Extracted from a 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) EVI dataset. - BD (Bulk Density): The soil bulk density. * Sand, Silt, Clay: The three main components of soil texture. - SOC (Soil Organic Carbon): The soil organic carbon content. * TN (Total Nitrogen): The total nitrogen content in the soil. - C/N Ratio: The ratio of soil organic carbon to total nitrogen. * AN (Available Nitrogen): The soil's available nitrogen content. - AP (Available Phosphorus): The soil's available phosphorus content. * Laboratory CH₄ Oxidation: The rate of soil CH₄ oxidation from laboratory incubations. - pmoA Abundance: The abundance of methanotrophs, indicated by the pmoA gene. * AGB: aboveground biomass. - BGB: belowground biomass . All soil samples for laboratory incubation and physicochemical analysis were collected from a depth of 0-10 cm. The same FieldSiteID_plot in different sheets represents the same replicate of the same site. The CH₄ prediction data at monthly intervals from 2001 to 2021 are the monthly CH₄ uptake maps of the Qinghai-Tibetan Plateau (QTP) predicted in our study. The file "Random Forest Model and Regional Upscaling" contains the R code used for building the Random Forest model and performing regional predictions of CH₄ flux. ## Code/Software All the data were processed using R. ``` This work is marked CC0 1.0 Universal. To view a copy of this mark, visit https://creativecommons.org/publicdomain/zero/1.0/ ```