Global implications of a low soil moisture threshold for microbial hydrogen uptake

Domain: neuroscience
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
The impact of increasing anthropogenic hydrogen (H2) emissions on Earth’s radiative balance depends on the soil microbial H2 sink—the largest and most uncertain term in the global H2 budget. Soil moisture is a primary but poorly quantified control regulating the soil sink. Here, we assess the sensitivity of microbial H2 oxidation to soil moisture in laboratory experiments with temperate and arid soils spanning distinct textures. We report H2 oxidizer activity down to –70 to –100 MPa water potentials across soils, which are among the driest conditions reported for microbial activity and are much drier than assumed in global simulations of H2. Using genome-resolved meta-omics, we link H2 oxidation dynamics in temperate soils to specific desiccation-adapted microbial taxa that contribute differentially to H2 uptake along the moisture gradient. Through global simulations, we show that our observationally constrained drier moisture threshold increases the contribution of arid and semi-arid regions for soil H2 uptake by 4-7 percentage points (pp), while decreasing the contribution of temperate and continental regions (−7 pp). Our results highlight the importance of H2 uptake under extreme hydrological conditions, particularly the roles of desertification, dryland expansion, and H2-oxidizer ecophysiology in modulating long-term changes in H2 uptake.

Introduction
Molecular hydrogen (H2) is an abundant trace gas in the troposphere, with a current global mean concentration of ~550 ppb in the marine boundary layer1. H2 is expected to play a key role in the global clean energy transition due to its potential as a low-carbon fuel2,3. However, an increasing reliance on H2 energy will likely raise atmospheric H2 concentrations, as H2 is leak-prone4,5. This warrants careful evaluation of the potential climate effects of increasing H2 levels in the atmosphere. While H2 is not a greenhouse gas, it still imparts a significant indirect radiative forcing due to its interaction with hydroxyl (OH) radicals in the atmosphere. This interaction prolongs the lifetime of tropospheric methane and contributes to the production of stratospheric water vapor and tropospheric ozone6–11. Given the significance of H2 for Earth’s radiative balance (100-year time-horizon Global Warming Potential, GWP100, estimated to be 11.6 ± 2.8 11), the balance of the global H2 budget is a critical consideration in the context of accelerated energy transitions.

Uptake by soil microbes is the largest sink for atmospheric H2, accounting for 60–80% of the global tropospheric H2 sink8,12–14. By reducing the amount of H2 available to react with OH radicals in the atmosphere, the soil sink considerably dampens the indirect radiative impact of H2. Recent culture-based and genomic work has established that soil uptake is microbially mediated, with the high-affinity hydrogen oxidizing bacteria (HA-HOB) being widely distributed and active across ecosystems15–21. The magnitude of the global microbial H2 sink is far from constrained and is the largest uncertainty in assessing the indirect global warming potential of H2 11.

One of the primary sources of uncertainty in soil sink estimates is the lack of quantitative constraints on the effects of soil moisture variability on H2 uptake22–27. This is due to the complex ways in which shifts in soil moisture affect H2 uptake, including direct impacts on microbial metabolism and the regulation of H2 diffusion into soil pores24,26. At the dry end of the moisture gradient, HA-HOB are water-stressed, resulting in limited H2 oxidation due to biotic constraints. Uptake is also low at higher moisture levels, due to the reduced diffusion of H2 in water-filled pores, a physical constraint on uptake. Thus, the relationship between soil moisture and H2 uptake is highly non-linear23,25,26.

Several attempts have been made to parameterize the soil sink to capture its sensitivity to soil moisture variability, along with other abiotic factors such as temperature and soil type8,23,25,26,28,29. Due to the paucity of observational constraints on soil uptake25,30–34, particularly in moisture-limited, arid, and semi-arid soils, these parameterizations result in inaccurate estimates of the seasonality and spatial variability of uptake8,27,35. Most parameterizations include a water-stress threshold, below which uptake is essentially zero; however, the magnitude of this threshold is poorly constrained8,29. Similarly, an optimal moisture level at which peak uptake occurs has been identified as a key threshold regulating microbial H2 oxidation, as above this level, uptake declines due to limited diffusion of H2 and O2 26. The uncertainty associated with these moisture thresholds makes it challenging to represent the spatio-temporal variability in the H2 sink strength in global models27.

In this work, we performed controlled laboratory experiments to decipher the mechanistic relationship between soil moisture and microbial H2 oxidation activity, focusing on conditions of negligible diffusive limitation. We analyzed three temperate soils and two arid soils from the Northeastern and Southwestern US, respectively. The temperate soils were sampled from three distinct ecosystems: a deciduous forest, a grass-shrub meadow, and an oak-pine forest. Across all soils, we measured H2 oxidation rates in moisture-controlled incubations. Based on the previously established non-linear relationship between soil moisture and H2 uptake, we expected the measured rates to be directly linked to the moisture response of HA-HOB, particularly under drier conditions when uptake is not diffusion limited. Thus, in incubations designed to minimize diffusive limitation for uptake, we determined the moisture sensitivity of HA-HOB by coupling measurements of water potential and microbial H2 oxidation rates. We then revised previous formulations of soil H2 uptake models based on the experimental data, and assessed the response of the global soil H2 sink, both spatially and temporally, to the new parameterization. We further analyzed the microbial community structure in our temperate soil incubations to specifically examine how the diversity, abundance, and relative activity of different HA-hydrogenases (i.e., enzymes mediating high-affinity H2 oxidation) and HA-HOB vary with moisture changes and soil type. Collectively, our findings provide mechanistic insights into how soil moisture regulates high-affinity H2 uptake to improve our understanding of the global soil H2 sink and its potential sensitivity to changing climate conditions.

Results
Moisture effects on microbial H2 oxidation across soil types
Here we report microbial H2 consumption measurements under controlled moisture conditions in three temperate soils—sandy loam and silty loam from a deciduous forest and meadow ecosystem, respectively, in central New Jersey, and loamy sand from the New Jersey Pine Barrens (PB). Hereafter, we refer to these as the forest, meadow, and PB soils, respectively. Following previous literature, we first report the results in terms of soil moisture. We then show that using the soil water potential rather than soil moisture offers better quantitative insights on the shared water-stress limitations for HA-HOB across soil types, as water potential provides a more intrinsic measure of water availability that is comparable across different soil textures. Specifically for soil moisture, we express the experimental saturation as the fraction of water-filled soil pores, where 100% represents saturation and 0% corresponds to fully dried (oven-dried) conditions. However, since even after oven-drying, residual water remains trapped in smaller soil pores, we stress that 0% does not signify a complete absence of water—a known problem in the soil hydrology community36. As later discussed, this is an important caveat to consider, especially when evaluating the moisture percentages in the dry range.

Experiments revealed the expected non-linear relationship between soil moisture and H2 oxidation activity in all three soils, with maximum uptake occurring below 40% saturation (Fig. 1a). H2 consumption was not observed in control incubations (i.e., autoclaved soils, air-dried soils, and incubations without soils; Fig. S2). Measurable H2 oxidation was observed under very low moisture levels, only slightly above air-dried conditions (Fig. 1a). Oxidation rates initially increased sharply with increasing moisture until reaching a maximum (Fig. 1a). Peak activity was observed at ~15%, 25% and 35% saturation in the PB, meadow and forest soils, respectively. Rates decreased above these moisture levels, indicating reduced microbial consumption of H2 (Fig. 1a), and slight diffusive limitation in the PB soil above 60% saturation (Fig. S1). In PB sand, a clear decrease in uptake rate was not evident, as higher targeted saturation levels did not result in higher actual saturation, and uptake rates remained similar across moisture treatments. This pattern indicates drainage from the sandy soil, as expected at saturations above 30% (Fig. S1), impeding higher saturation levels in the incubations.

Across all three soils, the forest sandy loam soils showed the fastest microbial H2 uptake with the highest measured rate constant (min−1 g-dry Soil−1) 4- and 5-fold higher than that of the meadow silty loam and PB loamy sand soils, respectively. Uptake was not observed under fully air-dried conditions, which were estimated to correspond to approximately 5%, 7%, and 1.3% saturation for meadow, forest, and PB sand soils, respectively (Fig. S2, S3; Table S1). Measurable uptake was observed under moisture levels only slightly above air-dried conditions: ~5% saturation in silty loam (meadow), ~7% saturation in sandy loam (forest), and ~2% in PB sand (Fig. S3, Table S1). However, there is large uncertainty in these moisture measurements in the very dry range due to the small amount of water present. For example, for the forest soil, the lowest moisture amendment for which uptake was detected was estimated to have the same moisture content as the air-dried soil at ~7.6% saturation (Table S1). This highlights methodological limitations in estimating moisture content by conventional methods (i.e., oven-drying the soil sample until constant weight). Residual water that may still remain in the soil makes estimating moisture content gravimetrically in the dry end of the spectrum especially challenging. We therefore also measured the water potential (Ψ) of these soils as a more intrinsic measure of water availability.

Unexpectedly low water stress threshold for high-affinity H2 oxidizers
Water potential (Ψ), a measure of the energy state of water, is widely used as an indicator of soil water availability to plants and microbes (e.g. 37–39). Our water potential measurements indeed showed that the air-dried samples had much lower water availability than those amended with moisture, even in the very dry range (Table S1). Moreover, the measurements revealed a low and consistent moisture condition for the onset of microbial H2 consumption, which we define here as the water-stress threshold, Ψws. In the forest soil, measurable H2 oxidation occurred at Ψ ~ -70 MPa, with no uptake in the air-dried soils at −130 to −140 MPa (Table S1). A similar Ψws was observed for the meadow soil, as the driest sample for which H2 oxidation was observed was at -65 MPa (Table S1). The PB sand soils were active under lower water potentials, down to approximately -90 MPa (Table S1). We performed some additional tests on two sandy soils from arid regions. These desert soils exhibited significantly lower H2 oxidation rates than the temperate soils in the moist regime, but their Ψws was similar to the temperate soils (non-zero oxidation rates measured for Ψ > -75 MPa, Fig. 1b, Table S2). Collectively, these results suggest the water-stress threshold of HA-HOB to be between –70 and –100 MPa, among the lowest thresholds ever reported for microbial persistence and activity37,38,40.

These measurements further allowed direct integration of our results into the recently developed mechanistic model for H2 uptake that leverages Ψ as a fundamental measure of moisture content26 (Fig. 1b). Notably, this approach eliminates the need for soil-specific moisture thresholds for water stress and optimal conditions in parameterizing H2 uptake as it directly links bacterial activity to thermodynamic considerations on water availability. Revising Ψws from −3 MPa, a value based on plant physiology used in the original model formulation26, to −100 MPa, as measured here, yielded significantly improved fits to the experimental data (Fig. 1b).

Uptake rates increase with soil carbon content
For the two loamy soils (forest and meadow), H2 oxidation rates scaled with average soil organic carbon (SOC) content within the drier range of the moisture gradient (Fig. S4a). Peak oxidation rate in the forest soil was approximately 4-fold higher than that in the meadow soil on a per gram basis, when not controlled for SOC content. However, this difference disappeared when the rates were scaled by mean SOC (g/g), as the scaled rates were essentially equivalent for the two loam soils below the optimal moisture thresholds (i.e., <30% saturation). The loamy sand (PB sand), in contrast, showed higher rates than the two loams when adjusted for SOC content (Fig. S4a), suggesting higher relative abundance and/or activity of HA-HOB in these soils. Maximum H2 oxidation rate (kmax min−1 g-Soil−1) increased nearly linearly with SOC (Fig. S4b).

Effects on the spatial and historical variability in H2 sink strength
To examine how the observed sensitivities of microbial H2 oxidation to moisture could impact the global H2 soil sink, we estimate the deposition velocity vd(H2) (Eq. 3) using a Ψws of -3 MPa and -100 MPa. In this model, vd(H2) accounts for both biotic and physical processes regulating the soil sink, with simulations run with and without modulation by SOC (Fig. S4b). We find that the particular value of Ψws employed in the model significantly affects the spatial distribution of modeled uptake rates (Fig. 2a-c)41. For Ψws = −3 MPa, soil moisture is predicted to be insufficient to support H2 uptake in ~90% of deserts and 45% of semi-arid regions. For Ψws = −100 MPa, the degree of uptake inhibition is reduced to 70% (deserts) and 20% (semi-arid regions), respectively. In humid areas, where moisture is generally high enough to support microbial H2 oxidation, vd(H2) is primarily controlled by diffusion26.

A lower Ψws for HA-HOB tends to shift H2 uptake from continental regions (-7 pp) to desert (+7 pp) and semi-arid (+4 pp) regions. The relative importance of these regions also impacts the predicted change in vd(H2) over the second half of the 20th century (Fig. 2d). In continental and polar regions, vd(H2) increase is insensitive to Ψws, reflecting increased H2 soil diffusion due to lower soil moisture, warming, and reduced snow cover8,27. In contrast, drying in desert and semi-arid regions has increased the areal extent of regions where soil moisture is too low to support H2 uptake. As deserts play a larger role with a lower Ψws, this contrasting response results in a smaller increase in vd(H2) (+~7%) with a drier Ψws of -100 MPa, compared to a wetter Ψws of -3 MPa (+~10.5%) from 1950 to 2019. Notably, a lower water-stress threshold of -100 MPa has a stronger effect on vd(H2) on interdecadal timescales compared to interannual timescales (Fig. S5).

We incorporated an SOC modulation into global simulations of vd(H2) using a linear fit given by kmax = 0.00387 + 0.0024*(SOC) to our data (Fig. S4b). Incorporating this modulation changes the relative contribution of continental regions, which are not moisture-limited, to H2 uptake by nearly 35% (Fig. S6). The simulated relative contribution of hot deserts to the H2 sink strength is also sensitive to SOC (Fig. 2; S6). Without SOC modulation (Fig. 2), the relative contribution of deserts to the overall H2 sink increases by 75%. This SOC-induced change in the spatial distribution of the H2 sink results in a weaker increase (20%) in the H2 sink strength from 1950-2019.

Differential moisture response of high-affinity H2 oxidizers
For each temperate soil type, we obtained metagenomes and metatranscriptomes at several moisture levels across the tested range (Table S4) to assess the community dynamics and ecophysiologies of high-affinity hydrogen oxidizers. Due to lower yield, replicate samples from PB sand and meadow soil were pooled before extraction. Putative high-affinity hydrogenase (HA-Hase) homologs were identified in both the contigs and metagenome-assembled genomes (MAGs) (see “Methods”).

Across all three soils, group 1h hydrogenase (Hhy) was the most dominant form of HA-Hases15; Table S4). Several Group 2a hydrogenases (with potential high-affinity activity17,42), were also recovered from the PB sand metagenomes, but these were significantly less abundant than the 1h hydrogenases (3 homologs of group 2a versus 297 of group 1h; Table S4). To estimate the relative abundance of HA-HOB in these soils, we compared the normalized abundances of HA-Hases with average normalized abundances of the single-copy marker genes for DNA gyrase, gyrA and gyrB. This analysis indicated that approximately 34% of the community in forest soil, ~25% in meadow soil, and 40% in PB sand have the ability for atmospheric H2 oxidation (Fig. S7). These estimates are comparable to previous surveys of HA-HOB in various ecosystems, which indicated the relative abundance of hydrogen oxidizers to range from 20 to 57% of the total community across diverse ecosystems43,44.

We next analyzed the relative expression (i.e., activity) of HA-Hases across moisture gradients in the three soils. For this analysis, we mapped metatranscriptomic reads to previously identified HA-Hase homologs. Mapped read counts were normalized by gene length and sequencing depth to obtain transcripts per million (TPM) values, providing a standardized measure of gene expression. In all three soils, active expression of HA-Hases was observed across all moisture levels (Fig. 3a). For the PB and Forest soils, TPM values did not differ significantly across moisture levels (Fig. 3a; Kruskal–Wallis test, p=0.134) and 0.787 each), but differed significantly for the Meadow soil (p=0.0004). We also assessed the combined total TPM of all HA-Hases at each moisture level, reflecting the community-wide magnitude of hydrogenase expression, and the median TPM across HA-Hases, which provides a robust measure of the typical expression level that is less influenced by highly expressed outliers. For the PB sand and forest soils, the total and median TPM values were relatively consistent across moisture levels (Fig. 3a-b), suggesting that HA-Hase transcription is broadly maintained across hydrological conditions in these soils. Total activity in the meadow soil generally increased with increasing moisture (Fig. 3c).

While the overall HA-Hase expression across moisture levels remained relatively constant in the forest and PB soils, there were notable differences in the specific hydrogenase genes being expressed at each moisture level across all three soils (Fig. 3d-f). In all three soils, specific moisture-adapted clusters of HA-Hases appeared to dominate at different moisture levels (Fig. 3d-f). This suggests moisture-related niche separation of HA-Hases, and locally contingent Hase populations. Furthermore, the lack of correspondence between total expression levels and measured HA-H2 oxidation rates (Fig. 1a and Fig. 3a-c) suggest that the moisture-adapted clusters of HA-Hases likely differ in their catalytic efficiency.

High-quality MAGs encoding HA-Hases (all group 1h) from the three soils were classified within the phyla Acidobacteriota, Actinomycetota, Eremiobacterota, and Chloroflexota (Fig. 4a). Despite recent evidence indicating atmospheric H2 oxidation by Archaea45, we did not find any archaeal MAGs with HA-Hase homologs. The specific genera within each phylum encoding HA-Hases differed across soil types (Fig. 4a). Relative abundances of several genera showed notable variations with moisture change. For example, in the forest soil, the Acidobacteriota genus UBA5704 was more abundant than all other HA-HOB genera under drier conditions, at ~7% saturation (Fig. 4a). The high-affinity hydrogenase in this MAG was also most highly expressed at 7% saturation (Fig. 4a). While the Eremiobacterota and Actinomycetota MAGs showed increasing relative abundances with increasing moisture in the forest soil, their HA-Hases were most highly expressed at the driest condition (Fig. 4a). These patterns collectively suggest their decreasing reliance on H2 oxidation with increasing moisture.

In the meadow soil, the two Actinomycetota lineages appeared to mediate H2 oxidation at two different moisture conditions (Fig. 4a). Similarly, the various lineages of Acidobacteriota and Eremiobacterota in the PB sand also showed diverging relative abundance patterns and variable expression of their HA-Hases with changing moisture. This further implies that different HA-HOB lineages mediate H2 uptake across varying moisture levels in this soil type as well (Fig. 4a).

Functional profiling of the recovered HA-HOB MAGs revealed that aerobic heterotrophy was the predominant metabolic trait (Fig. 4b). While the capacity for the uptake of simple sugar compounds is relatively sparsely distributed, many of the MAGs harbor the potential for the degradation of complex carbon, including polyphenols and polysaccharides such as starch, xylan, and cellulose (Fig. 4b). These observations align with previous findings identifying HA-HOB as key players in the breakdown of complex plant-derived carbon in soils46,47. Flagellar motility is widespread among the PB sand MAGs (Fig. 4b)—likely advantageous in the highly varying moisture regimes characteristic of sandy soils with low water retention. Additionally, nearly all MAGs recovered from the PB sand and forest metagenomes also possess the ability for osmoprotectant transport, specifically for glycine betaine (Fig. 4b), which likely enables these HA-HOB to resist desiccation under dry conditions. H2 oxidation likely provides an additional way to resist desiccation via the production of metabolic water, as has been hypothesized before43,48. Notably, many of the MAGs also possess the ability for carbon monoxide oxidation (Fig. 4b), highlighting their capacity for versatile trace gas metabolism. Overall, functional profiling of the HA-HOB MAGs suggests that they possess multiple metabolic and physiological strategies—including the ability to oxidize atmospheric H2—that likely enables persistence and survival under challenging environmental conditions.

Methods
Soil collection, characterization, and experimental setup
The temperate soils were collected from three distinct ecosystems: a deciduous forest, a managed grassland shrub meadow, and a sandy oak-pine forest. The forest and meadow ecosystems are located within the Watershed Research Institute in Pennington, New Jersey (40.355010, -74.774580, and 40.353600, -74.774330, respectively). The oak-pine forest is located within the New Jersey Pinelands/Pine Barrens, and we sampled in the Brendan T. Byrne State Forest (39.886830, -74.536630). The arid soils were collected from two locations in Arizona (AZ soil 1: 34.736700, −111.985410; AZ soil 2: 33.969320, −112.129240). At each location, soil samples (<1 kg) were collected from three sub-locations spaced approximately 1 m apart, sampling within the top ~10 cm, after removing the living organic layer, if present. Subsamples from each location were combined into a composite sample by mixing in pre-cleaned (acid-washed, UV-sterilized) sealable plastic bags and stored on ice while transporting back to the laboratory. At the lab, soils were spread into a thin layer on a clean tray lined with an acid-washed and UV-sterilized plastic bag, any large plant material was manually removed, and the samples were left to air-dry for up to 3 days (until visually dry). The air-dried soils were sieved through a clean 2 mm sieve and stored in pre-cleaned sealable polyethylene bags in the refrigerator. All moisture incubations were set up within 10–15 days of sieving and storage.

Before setting up the moisture incubations, the sieved soils were characterized for soil texture, pH, porosity, bulk density, and gravimetric moisture content. Soil texture analysis revealed the forest soil to be sandy loam, the meadow soil to be silty loam, and the Pine Barrens soil to be loamy sand. The two arid soil textures were determined to be loamy sand and sandy loam. All three temperate soils were acidic (forest soil pH 4.2, meadow soil pH 4.6, PB sand pH 4.1). Measured porosity values for the sieved soils ranged from ~0.6 for both the meadow and forest soils, ~0.4 for PB sand, and ~0.5 for the two arid soils. Gravimetric moisture content (i.e., g water/g dry-soil) was determined by oven-drying soil at 105°C until constant weight. Volumetric moisture content was then estimated by multiplying gravimetric moisture content by bulk density.

Soil organic matter content was estimated using the loss on ignition (LOI) method. Briefly, triplicate air-dried soil samples were oven-dried at 105°C until constant weight to remove moisture. The dry weight was recorded, after which samples were combusted in a muffle furnace at 505°C for 4 h to oxidize organic matter. LOI (%) was calculated as:

LOI(%) = (Initial dry weight - Combusted weight) / Initial dry weight * 100 (1)

LOI values were converted to soil organic carbon as 63,64:

SOC(%) = 0.58 × LOI(%) (2)

To set up the moisture-amended incubations, we added a small amount of air-dried soil (2 g each of forest, meadow, and arid soils, and 4 g of PB sand) into acid-washed and autoclaved 240 ml serum bottles. We then slowly added ultrapure water to the soil to achieve specific moisture levels (3 replicates per moisture level), mixed by gently shaking the bottle. A sterile autoclaved polypropylene spatula was occasionally used to gently mix the soil to facilitate water distribution. The bottle was quickly sealed with parafilm to reduce evaporative losses, wrapped in aluminum foil, and incubated at 22°C. The acclimation period lasted ~6 days (i.e., all measurements were taken within 5–7 days of incubation) for the temperate soils. Any evaporative water loss during the incubation period was assessed by weighing the bottles daily. No measurable weight loss occurred during the incubation period. We further determined that the experiments were not diffusion-limited in the dry range (Fig. S1), as described in Supplementary Methods. The arid soil incubations were acclimated overnight before H2 oxidation measurements.

H2 oxidation measurements and soil moisture characterization
For H2 oxidation rate measurements, incubation bottles were retrieved from the incubator, flushed with room air, and immediately closed with a butyl rubber stopper, pre-boiled in sodium hydroxide, and crimped with aluminum seal. Experiments were conducted at ambient H2 concentrations at 22°C. Time zero sample (2 mL) was immediately drawn from the bottle headspace using a luer-lock syringe and injected into a reducing compound photometer (RCP) gas chromatograph (Peak Laboratories LLC) fitted with a 1 mL sample loop. Additional headspace samples (2 mL) were collected and immediately measured on the GC-RCP until an exponentially decreasing trend in H2 concentrations was captured. Triplicate incubations for each soil type-moisture combination were measured in parallel, with headspace samples sequentially drawn and analyzed from each replicate. Most experiments lasted under 2 h. The AZ soils were much slower in consuming H2, and therefore, the experiments ran substantially longer (~9 hr for AZ soil 1, ~50 hr for AZ soil 2). The limit of detection for the measurements was determined to be 57 ppb. Accordingly, the limits of detection for H2 oxidation rates were approximately 0.1 ppb min−1 g-Soil−1 (for incubations containing 2 g soil; forest and meadow soils), 0.04 ppb min−1 g-Soil−1 (for PB soil incubations containing 4 g soil), 0.05 ppb min−1 g-Soil−1 for AZ soil 1, and 0.01 ppb min−1 g-Soil−1 for AZ soil 2. Headspace H2 drawdown was modeled as a first-order decay process, and the rate constants (min−1) were used as a proxy for the rate of H2 oxidation. Initial H2 concentrations matched the corresponding ambient H2 levels measured for each experiment. For each soil type, killed controls (i.e., containing autoclaved air dried soil) and no-soil (empty) controls were also measured for H2 uptake (Fig. S2).

Immediately following uptake measurements, the bottles were briefly sealed with parafilm again and re-weighed to determine evaporative losses during measurement. We did not observe any measurable weight loss during measurements. Soil was quickly subsampled into pre-weighed receptacles for: (i) re-measuring gravimetric water content (0.5 to 1 g soil), (ii) water potential measurements, and (iii) DNA/RNA. Approximately 1-2 ml of soil (~0.5–1 g) from the incubations was used for water potential measurements using the WP4C dewpoint potentiometer (METER Group). Soil subsamples for DNA/RNA (~0.3-0.5 g each per replicate) were sampled into sterile cryovials and frozen at -80°C promptly following sampling.

Nucleic acid extractions and sequencing
Microbial community analysis —including nucleic acid extraction, sequencing, and bioinformatic processing—was conducted for the temperate soils using widely established and standard protocols as described below. Total nucleic acids (DNA and RNA) were co-extracted (in two separate fractions) by combining the RNeasy PowerSoil Total RNA kit with the RNeasy PowerSoil DNA Elution kit (Qiagen), using manufacturer’s protocols. Quality and yield of the extracted DNA and RNA samples were assessed using NanoDrop and Qubit fluorometer respectively. For the forest soil, replicate soil samples from each moisture perturbation (~0.3-0.4 g) were extracted separately as these samples generally yielded high DNA/RNA concentrations (Table S3). In contrast, for the meadow soil and PB sand, triplicate soils samples per moisture amendment were pooled prior to extraction (~1.2 g for pooled soils; Table S3). DNA extracts were cleaned using the QIAGEN DNeasy PowerClean Pro Cleanup kit to improve quality and were re-measured on the NanoDrop to ensure minimal impurity levels in the final extracts. Extracted nucleic acids were stored at -80°C until further processing.

DNA and RNA samples corresponding to 4–5 moisture levels for each soil type were used for metagenome and metatranscriptome sequencing (Table S4). Total DNA and RNA samples for the selected samples were sent to the Princeton Genomic Core facility for paired-end sequencing on the Illumina NovaSeq platform (S1 300 nt flow cell). For the RNA samples, a ribo-depletion step was performed prior to library construction. The resulting reads were adapter-trimmed and demultiplexed by the sequencing facility. Further processing of the reads was carried out using the Princeton Research Computing cluster.

Processing and analyses of metagenomes and metatranscriptomes
Demultiplexed metagenomic reads were quality filtered using FastQC (v0.11.9)65 and Trimmomatic (v0.39)66. Metagenomes for each soil type were co-assembled using MEGAHIT (--min-contig-len 1000; v1.2.9)67,68. Bowtie 2 (v2.3.5)69 was used to map reads back to the assembled contigs. Contig gene-calling was performed by using Prodigal (v2.6.3)70 and the resulting gene sequences were used as input for DIAMOND blastp (v2.1.10)71 search against the UNIPROT-SWISSPROT database to obtain functional annotations. Contigs longer than 1500 bp were binned using MetaBAT2 (v1.12.1)72 and MaxBin2 (v2.2.7)73. Bin refinement was carried out using the bin_refinement module in MetaWRAP (v1.2)74. Prodigal (v2.6.3)70 was used to obtain amino acid sequences of gene calls. CheckM (v1.2.3)75 was used to assess the quality (>70% completeness and <10% contamination) of the recovered genomes for downstream analyses. Taxonomic annotations were obtained by using the Genome Taxonomy Database toolkit (v.2.4.0)76,77, classifying against the R220 database.

Metatranscriptome reads were QC-filtered to remove adapters by using FastQC (v0.11.9)65 and Cutadapt (v3.7)78. Using Bowtie 2 (v2.3.5)69, QC-filtered reads were mapped to the metagenomic contigs obtained for each soil type. Relative abundance estimates for contigs and metagenome-assembled genomes (MAGs) were obtained by using CoverM (v0.7.0)79. For abundance estimates of MAGs in the metagenomes, Coverm “--method rpkm” was used. Conversely, “--method tpm” was used for estimating relative transcription of the MAGs in the metatranscriptomes. Additional functional annotations for MAGs were obtained via DRAM (v0.1.2)80 and RASTtk (v1.073)81 as implemented within the KBase platform82. KO annotations for translated proteins in each MAG were obtained using GhostKOALA83. DIAMOND BLASTp71 searches against databases generated for each hydrogenase group from HydDB84 were used identify putative hydrogenase homologs in MAGs and contigs. Hits were aligned with reference sequences using Mafft (v7.475)85, which were filtered using trimAl (v1.4.1; “-gt 0.8 -cons 60”)86, then used to compute phylogenetic trees using FastTreeMP (v2.1.10)87. Potential false positives were filtered out based on phylogenetic placement, homology, and residue comparisons.

Metatranscriptomic reads were mapped to the high-affinity hydrogenase homologs identified in the metagenomic contigs. Mapping was performed using Bowtie 2 (v2.3.5)69 and Samtools (v1.9)88. Mapped read counts were normalized by gene length and total metatranscriptome read counts to obtain transcripts per million (TPM) values for each gene homolog. Relative expression of high-affinity hydrogenases in the three soils was compared across moisture levels by using heatmaps (ComplexHeatmap package in R) with k-means clustering. To determine an appropriate number of clusters (k), we applied the silhouette method using the fviz_nbclust() function from the factoextra R package, which evaluates clustering performance across different k values based on average silhouette width. Based on this analysis, k=4 was selected as the optimal number of clusters. K-means clustering was then performed on z-score–normalized TPM values using the kmeans() function in R with 25 random starts to ensure stability of the clustering solution. Statistical differences in TPM across moisture levels within each soil type were assessed using the Kruskal–Wallis test with p-values adjusted for multiple comparisons.

Global estimates of H2 deposition velocity, vd(H2)
The deposition velocity of H2 (vd(H2)) is calculated with the solution of the vertically explicit H2 mass balance in soil89,90 as:

1 / vd(H2) = 1 / gi + 1 / gs (3)

where gi represents the H2 conductance through possible diffusive barriers (e.g., snow cover, canopy), which reduces H2 transport to HA-HOB, and gs is the conductance through the active soil layer where H2 oxidation by HA-HOB occurs. vd(H2) in this model accounts for both biotic and physical processes regulating the soil sink. gs can be expressed as:

gs = kmax * f(s) * g(Ts) * h(SOC) * Ds (4)

where f(s), g(Ts)23, and h(SOC) are the sensitivity of microbial H2 oxidation to volumetric soil moisture (s), temperature (Ts), and SOC, respectively26. Ds is the diffusion of H2 in the soil and kmax is the maximum oxidation rate of H2 by HA-HOB. f(s) is parameterized following Bertagni (2021)26 as:

f(s) = (s - sws) / (sopt - sws) * (1 - (s - sopt) / (1 - sopt)) ^ β2 (5)

where sws is the lower moisture threshold for bacterial activity, and sopt is the optimum. These soil-specific moisture values can be derived using only two water potential values (for bacterial inhibition and optimum) and the soil water characteristic curves. For our simulations, these water potential values are derived from the experiments. N is a normalization factor such that max(f) = 1 * h(SOC) was empirically derived based on the experiments performed in this study as a linear fit between maximum of H2 oxidation rate for each soil type and mean SOC (%) (described in Results).

1 / gi = 1 / gsnow + 1 / gcano (6)

Term gi reflects the transport of H2 through the canopy (gcano) and through snow (gsnow); gsnow was calculated from snow depth assuming a snow porosity of 0.645; gcano was prescribed following 91. Given the importance of short-term hydrological fluctuations26, we used hourly soil moisture, temperature, and pressure from ERA5 reanalysis92,93. The time-invariant spatial distribution of SOC was prescribed following SoilGrids v2.0 (top 5 cm94).

kmax was optimized for each configuration, such that the global deposition average vd(H2) is 0.09 mm/s for year 20108.

All statistical analyses and plotting were done in R (v4.4.195) and Python.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Dear Authors and Editors,\nReji, Bertagni, Paulot, Qin and Zhang present new measurements combined with analysis that offers a wealth of new insight\ninto soil microbial uptake of hydrogen. The authors correctly identify that this soil sink remains poorly constrained. They\nconduct new measurements that inform how the soil sink is related to the availability of soil water to microbes. The analysis\nthen identifies contenders for hydrogen oxidising microbes present in the tested soils and, by inserting their water potential\nconstraint into a theoretical soil uptake model, probes the planetary distribution of soil uptake by climate zones.\nI consider that this work is a useful contribution to our understanding of the hydrogen soil uptake in low-moisture soils. The\nparticular significance of their results comes from their finding that hydrogen oxidising microbes uptake hydrogen even when\nwater is much harder to obtain from soils than the wilting point of plants. I expect that there is wide interest in how this\nunderscores the fascinating nature of hydrogen as an important energy source for microbes in the most challenging\nenvironments. For those of us focused on the hydrogen budget, this work has important implications for how soil uptake may\nbe included in atmospheric chemistry simulations – the authors find a more important role for uptake in arid regions – and\nmay carry implications for policy-important hydrogen GWP100 calculations.\nOne disagreement between this study and an ongoing hydrogen uptake study (Drewer, Cowan, Nemitz et al., shared\npublicly: https://h2envimpacts.org.uk/environmental-impacts-of-hydrogen-energy-meeting/ :\nhttps://drive.google.com/drive/folders/1Dvm5pzbnL8D-mA3EMzFTqnEVVAKKYUKB ) is in their choice of just several\ngrammes of soil (ln 180-210) compared with ‘~800 g’, ‘packed to roughly field bulk density’, in the Drewer et al. method.\nPlease can the authors explain why they choose such a small mass. Additionally, please can they comment on the\nimplications for diffusive or soil-moisture dynamics of having such a relatively large surface-area to volume in these\nexperiments compared to others.\nCan the authors include an explicit comparison between their results and with the recent work of Paulot et al. (ACP,2024),\nwhere similar schemes with Psi_ws = -3 MPa and -10 MPa are compared using GLDAS data instead of ERA5 hourly (ln\n300)?\nAn additional analysis that would boost the importance of this work is a quantification of the sub-decadal variability in soil\nuptake with water-stress at -100 MPa compared with water-stress at -3 MPa. This is a small extension from their calculated\n1950-2019 uptake change (ln 438-441) that would provide useful insights into a poorly understood variability of atmospheric\nhydrogen.\nSigned cordially,\nAlex Tardito Chaudhri\nMinor comments:\n(ln 39) suggestion: these water potentials could be contextualised, e.g. ln 354,535 -3 MPa is mentioned as the wilting point\nfor semi-arid plants.\n(ln 69-70) different estimates vary from 60s%-80s% so I’m not convinced that we can confidently state that soil uptake is\ncertainly over 70% of sink. E.g., based on atmospheric hydrogen’s isotopic signature Rhee et al. 2006 get over 80%, but\nbased on some assumptions and what works in their models the Sand et al. 2023 estimate c.70% but with some models less\nthan that.\n(ln 89-90) this mention of the ‘paucity of observational constraints’ is very important, and I am impressed that the authors\nseek to address this. Suggestion: however, there is a relative abundance of measurements in temperate NH America, so is it\nworth highlighting the particular paucity of soil flux measurements in (semi-)arid soils?\n(ln 126,127,174) there is a lack of consistency in lat-lon reporting. For usability, I would suggest reporting in degrees with\ndecimals in both cases, and for decimal places is it important or accurate that a location is reported to a precision equivalent\nto one metre?\n(ln 126,127) reliability: are there issues with collecting samples in single locations in your sample areas? I understand that\nin-situ flux chamber measurements show enormous spatial variability uptake within metres of each other (e.g. Cowen et al.\nEGUsphere preprint-2024).\n(ln 154) hyphen formatted as minus sign.\n(ln 196-198) suggest these rates would be clearer to understand in ppb/min/g (to check – sometimes units formatted gSoil,\nother times as g-Soil).\n(ln 198-200) is this as trivial as discussed here? Chamber uptake of hydrogen is seen to occur on very short timescales,\nplease explain how this model of drawdown is robust.\n(ln 215-266) I indicate that these sections are beyond my expertise. Suggestion: is it worth commenting how widely\nestablished these methods to give context to readers without this expertise?\n(ln 317-318) suggestion: considering your helpful phrasing about the intrinsic nature of \\Psi (ln 354-355,379-381), could the\nlink be better explained here? Also, do your ideas in ln 550-553 not give some good justification for why you might want to\nmake this link sooner?\n(Fig. 1) are the scattered points the same tests in each panel, can the triangles, circles, squares in a also be used in b. The\nlegend in b shouldn’t have the scatter marker lying on a line.\n(ln 434-436) please see commentary above regarding quantifying sub-decadal variability of uptake.\n(ln 477) typo ‘to be range from’\n(ln 484) indicating\n(ln 498-499) suggestion: is it worth commenting on how usual/unusual it is to find a novel genus in such tests, e.g. is it\nconvincing that it is a novel genus rather than a measurement error?\n(Fig. 3) Clarity: as each genus can only be in one phylum please can the legend be simplified to list the genera under sub-\nheadings for the phyla with the correct symbols (currently they all look like acidobacteriota in the legend and it is hard to\ndetermine colours). Then can you omit the ‘p__’s and ‘g__’s? Is ‘Flgellum’ a misspelling and should ‘flagellin’ have a capital\nF?\n(ln 576-578) this might be true in the sub-tropics and tropics, and presumably also when you integrate everywhere, but can\nbe potentially misleading if soil moisture is not the primary driver everywhere. In (23) and (26), h(T) has a steep gradient\nbetween 0 and 20 degC. (35) see that the observed hydrogen distribution can be explained where NH extra-tropics mostly\nrespond to temperature seasonality.\n(Remarks on code availability)\nhttps://github.com/Linta-Reji/Reji2025_H2_SoilMoisture provides clear instructions and data files are available. (I have\nsearched through the respository but not have not run the code in R).", "reviewer_2": "Journal-specific considerations\n- What are the noteworthy results?\nThis article is the first to systematically relate soil H2 uptake to water potential, and the authors show that H2 oxidation by\nmicrobes occurs at some of the lowest water potentials of any known microbial process. The authors implement new\nunderstanding of the relationship of H2 uptake and soil water-potential into a global model, which shows that arid lands\nlikely make a relatively larger contribution to the global H2 sink\n- Will the work be of significance to the field and related fields? How does it compare to the established literature? If the work\nis not original, please provide relevant references.\nYes, this work represents a significant advancement to our understanding of the moisture sensitivity of overwhelming soil\nsink for atmospheric H2. The field is limited in data, and this systematic study that was well-designed to meet the needs for\nupscaling with models makes an important contribution to our understanding of its major driver of variability.\n- Does the work support the conclusions and claims, or is additional evidence needed?\nYes.\n- Are there any flaws in the data analysis, interpretation and conclusions? - Do these prohibit publication or require revision?\nWe suggest that the authors focus their analysis of metagenomes and metatranscriptomes more squarely on the\nhydrogenase genes and their expression rather than predominantly on MAGs (see Major Comments below). The analysis of\nMAGs could be extended to better understand how the organisms are physiologically responding to moisture limitation.\nThere are also interpretations related to SOC that are unclear. The authors could revise based on the feedback to make the\npaper suitable for publication.\n- Is the methodology sound? Does the work meet the expected standards in your field?\nYes.\n- Is there enough detail provided in the methods for the work to be reproduced?\nNo, but the missing points are minor and could be added in a revision.\nSummary\nReji et al., demonstrate that H2 oxidation occurs at some of the lowest water potentials known for microbial metabolism and\nrevise H2 sink models based on these results. The authors integrate these findings into a global model and show that arid\nregions contribute disproportionately to the global H2 sink, which has significant implications for climate-relevant H2 cycling.\nA key strength of the paper is the scaling of measurements from the microbe to global scales and integration of lab-based\nand modeling approaches. The determination of a new, lower water potential threshold is also a notable contribution. A\nweakness of the paper as presented is its interpretation of metagenomic and metatranscriptomic data is underdeveloped,\nwith an overreliance on MAG-level analyses and limited attention to gene-specific information (hydrogenase and\notherwise). Methodological clarity is also somewhat lacking, particularly in how omics replicates were handled (particularly\npooling of replicates), and the global modeling results would benefit from clearer explanation findings that appear somewhat\ncounterintuitive in the discussion. Despite these issues, the core dataset and modeling framework are strong, and the paper\nmakes a substantial contribution to the field. We recommend major revisions with a special focus to strengthen the omics\ninterpretation and clarify key methodological and conceptual points.\nMajor comments\n1) A more thorough exploration of the omics data\nIt sounds like DNA/RNA was extracted from individual forest replicates but from pooled meadow and pine barrens soil. It\nneeds to be explicitly stated in the methods how DNA/RNA were pooled before sequencing and whether any replicates\n(e.g., for the forest) were retained. It doesn’t appear that they were, which means that all downstream analyses cannot\ninclude replicate-based statistical tests. Please discuss how the lack of statistical evaluation limits the interpretability of the\nresults. There are error bars on Figure S6, but these appear to be internally derived from the stdev on the mean of n=2 genes\nwithin a given metagenome.\nA primary concern is that the majority of the analysis of the genomic data are not directly related to the activity of the\nhydrogenase genes but are more focused on overall measures for the MAGs. Could the analysis be improved by\nimplementing a moisture-based analysis of any of the following suggestions?\n(i)Could the expression levels of Hase in the entire sample, including unbinned contigs, be plotted to show overall\ncommunity responses (like Fig. S6 but for expression)?\n(ii)Instead of showing the expression of the MAGs as a whole (which needs to be defined more clearly in the methods\nexactly what that means and how it is determined), could Fig. 3b show the patterns in expression of the Hase in those\nMAGs?\n(iii)The paper would benefit from an exploration of comparisons between transcript levels of the H2 oxidizing genes and the\nother pathways of interest. Could the relative expression of Hase vs other genes including the osmoprotectant genes (to\nsupport statements in L591) and C-cycling genes (L509-515) in Fig. 3b be evaluated on a per-MAG basis?\n(iv) Overall, the patterns in Hase MAG abundance and expression were not sufficiently discussed. Given that such a high\nproportion of organisms appear to encode for these Hases (Fig. S6), it is unclear how much can be derived from the pathway\nrepresented in 12 MAGs (Fig. 3b), especially without comparison to the pathways represented in the other 24 MAGs\nrecovered without Hases. To support Fig. 3a, we think it's necessary to not only look at those bins which contain the\npotential for H2 oxidation, but also those other MAGs that don't. It would support claims about the functional roles of H2\nmetabolizers through comparison with the general microbial community or perhaps\nphylogenetically similar organisms that do not encode the H2 metabolism trait.\n(v)The relationship of these findings could be plotted versus water potential to more clearly evaluate their role in driving H2\noxidation, as you nicely show in Figure 1b.\n2) Relating H2 oxidation to soil carbon\nThe discussion of the ecological strategies for H2 oxidizers in low versus high C soils would benefit from additional\nclarification and organization–especially to help explain apparent contradictions. The first part of the discussion in L580 on\ncarbon-rich soils appears to conflict with the later part in L584 on oligotrophic soil. If the H2 uptake per unit SOC is higher in\nthe non-forest soils, how do we come to the conclusion that there is a higher reliance of the microbial community on\nalternative energy sources such as H2 in the forest soil? It could help to define whether ‘higher reliance’ means as a\ncommunity average or a per cell average? Could the authors add data from other papers to Fig. S4b to support the linear\nmodel. With three data points distributed, it is easy to get a high R2 and assume a linear form.\n3) Clarification of global projections for deposition velocity\nThe results and discussion on the change of deposition velocity globally require additional clarification (Fig. 2). Specifically,\nthe paper does not explain why lowering the water potential threshold (e.g., from -3 to -100 Mpa) would decrease deposition\nvelocity in continental regions. It is unclear whether this somehow shifts the model relationships (given by equations in this\nMS and Fig. 2 in Bertagni et al., 2021) or through some other interaction. Physically, what could cause microbes to be able to\naccess H2 at even lower moisture levels but reduce the deposition velocity of H2 overall? We understand that Fig. 2c is\ncompositional (%; this figure would be clearer if plotting the absolute values instead of percentage data) so the decrease in\ncontinental could just be at the expense of the desert increasing. But continental values for vdep are also decreasing in an\nabsolute sense in Fig in 2b vs 2a, and this is not sufficiently explained. Does this new formulation not only allow more\nuptake at low moisture levels but also temper the amount of uptake at higher moisture levels too? Along these lines, the\nimplications for the projected change in vdep over time should be discussed more thoroughly.\nMinor comments\nWe understand there are trade-offs in selecting the timeline of the experiment. Please discuss the impact of the duration of\nthe incubations relative to the possible growth rate of these organisms in the manuscript.\nTemperate forests and grasslands are some of the best represented by current data, and the authors should explain why\nthese sites were chosen. How could the fact that your sites are all acidic influence your results and conclusions?\nL44: Define pp in the abstract.\nLines 75-76: “The global warming potential of H2” should likely also indicate that this is “indirect”.\nLines 87-91: Might be beneficial to expand a bit on the work that has been done rather than essentially just saying it has\nlead to inaccurate estimations.\nL117: Ending the introduction with a statement on what the big-picture importance of the work could be would be more\ncompelling than ending by listing another method.\nL126: Since you have two forest sites, could you use more specific shorthand names rather than forest vs PB?\nLines 129-130: “At each location, soil samples were collected from the top ~10 cm, after removing the organic layer,\nif\npresent.” Would we expect the organic layer to not impact H2 diffusion in natural systems? Both by abiotic (diffusion\nlimitation) and biotic (H2 oxidation) mechanisms.\nL200: Please provide full details for your H2 oxidation rate calculation. Please give a range of estimated fluxes for\ncomparison to other studies. Specify what you used as the initial H2 concentration to do so and why (one concentration for\nall samples or did it vary between samples as initial H2 measurement)?\nL207-209: Give soil mass to be consistent with what you stated you added to the jars. How much soil was used for the\ngravimetric moisture analysis? Was it enough to determine significant shifts in moisture? Describe any limitations in the\nwater potential measurement for such a small amount of soil.\nL228: Is this the right table? It is not clear from this combination of tables what the moisture levels were for the samples\nsequenced, unless it is all the samples in Table S1. Please clarify in the caption.\nL340: Fastest microbial H2 uptake\nL363: It would be interesting to discuss these observations of the impact of recently added water to H2 oxidation in light of\nthe intermittent H2 uptake concepts presented in Bertagni et al., 2021.\nL463: We have seen H2ase used more frequently (10.1111/j.1574-6976.2001.tb00587.x. ) than Hase\nL484: Check grammar\nFig. 1: ‘Faded’ colors are pretty hard to distinguish. Could you visualize in a more specific way? It’s not clear initially that the\nAZ soils are represented by a range of bars. We think the problem is that they are located up near all the other legends.\nCould you differentiate the legends from the ranges, perhaps with a box around the legends? We don’t think they need to\nhave separate colors since there are no points to look for in the plot–they just add confusion. Clarify whether or not these\nbars relate to the y axis as well. What does ‘/porosity’ mean in the caption? This should be placed in methods instead:\nSaturation levels (%) were estimated as volumetric moisture content/porosity.\nFig. 2: Is there a way that you could annotate Fig. 2d to indicate what a positive vs negative change means? We are\nassuming positive is an increase in vd in the later period vs the earlier period, but we wonder if you could annotate that to be\nmore clear? At first, we were confused whether c and d corresponded with a and b, and others might be too. It might also be\nuseful to adjust the axis labels on the bar charts so that they are centered beneath the bears rather than far to the side of\nthem.\nFig. 3b: Check spelling “Flgellum”\nTable S1: Indicate if water content is gravimetric % saturation.\nFig. S4: H2 oxidation rates increase with\n(Remarks on code availability)\nWe were unable to access the code. The github link did not connect to us to a functioning page.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to Reviewer Comments\nReviewer #1 (Remarks to the Author):\nDear Authors and Editors,\nReji, Bertagni, Paulot, Qin and Zhang present new measurements combined with\nanalysis that offers a wealth of new insight into soil microbial uptake of hydrogen. The\nauthors correctly identify that this soil sink remains poorly constrained. They conduct\nnew measurements that inform how the soil sink is related to the availability of soil water\nto microbes. The analysis then identifies contenders for hydrogen oxidising microbes\npresent in the tested soils and, by inserting their water potential constraint into a\ntheoretical soil uptake model, probes the planetary distribution of soil uptake by climate\nzones.\nI consider that this work is a useful contribution to our understanding of the hydrogen\nsoil uptake in low-moisture soils. The particular significance of their results comes from\ntheir finding that hydrogen oxidising microbes uptake hydrogen even when water is\nmuch harder to obtain from soils than the wilting point of plants. I expect that there is\nwide interest in how this underscores the fascinating nature of hydrogen as an important\nenergy source for microbes in the most challenging environments. For those of us\nfocused on the hydrogen budget, this work has important implications for how soil\nuptake may be included in atmospheric chemistry simulations – the authors find a more\nimportant role for uptake in arid regions – and may carry implications for policy-\nimportant hydrogen GWP100 calculations.\nOne disagreement between this study and an ongoing hydrogen uptake study (Drewer,\nCowan, Nemitz et al., shared publicly: https://h2envimpacts.org.uk/environmental-\nimpacts-of-hydrogen-energy-\nmeeting/ : https://drive.google.com/drive/folders/1Dvm5pzbnL8D-\nmA3EMzFTqnEVVAKKYUKB ) is in their choice of just several grammes of soil (ln 180-\n210) compared with ‘~800 g’, ‘packed to roughly field bulk density’, in the Drewer et al.\nmethod. Please can the authors explain why they choose such a small mass.\nAdditionally, please can they comment on the implications for diffusive or soil-moisture\ndynamics of having such a relatively large surface-area to volume in these experiments\ncompared to others.\nResponse: We thank the reviewer for these insightful comments. The use of a small\nsoil mass in our incubations was specifically employed to eliminate/minimize diffusive\nlimitation for H uptake. This is because our goal was to determine specific quantities\n2\nrelated to HOB activity, namely the moisture threshold and the uptake rate, which carry\ncritical implications in the biogeophysical understanding of the H soil sink and bacterial\n2\ncommunity but currently lack quantitative constraints. Reducing the amount of soil was\ntherefore essential to ensure that measured rates reflected potential biological activity\nrather than diffusion limitations. As shown in Figure S1, the concentration of H in soil\n2\npores and bottle headspaces remained consistent across moisture levels in the dry\nrange, supporting the conclusion that diffusive limitation was minimal in our incubations.\nThis is now more clearly emphasized in the manuscript at lines 96-98, 105-106, 172-\n173 (revised manuscript).\nCan the authors include an explicit comparison between their results and with the recent\nwork of Paulot et al. (ACP,2024), where similar schemes with Psi_ws = -3 MPa and -10\nMPa are compared using GLDAS data instead of ERA5 hourly (ln 300)?\nResponse: Thank you for these comments. We used ERA5 instead of GLDAS for two\nreasons: (1) GLDAS generally includes higher moisture estimates, which are not\nnecessarily realistic, especially in very arid regions. With a lower moisture threshold for\nbacterial uptake, as highlighted by our experiments, the GLDAS dataset would result in\nnearly all arid regions, including the Sahara, being active for H uptake (Fig. 1a below).\nlimits of H uptake in real desert environments since the Sahara Desert hosts very little\n2\nmicrobial biomass. Constraining these will require direct field measurements. Generally,\nuse of GLDAS results in a larger increase in vd(H ) with a lower moisture threshold of -\n2\nthreshold (Fig. 1b below). (2) GLDAS data begins in the 1980s, whereas ERA5 allows\nfor a longer temporal comparison.\nFigure 1: Comparing the effect of dataset choice for estimating vd(H ). (a, top panel):\nMPa) using GLDAS vs. ERA5. Most regions are above the threshold (at -100 MPa)\nwhen using GLDAS. (b, bottom panel): H deposition velocity across time estimated\n2\nusing ERA5 vs GLDAS for two water-stress thresholds (blue: -100MPa and red: -3 MPa\n).\nAn additional analysis that would boost the importance of this work is a quantification of\nthe sub-decadal variability in soil uptake with water-stress at -100 MPa compared with\nwater-stress at -3 MPa. This is a small extension from their calculated 1950-2019\nuptake change (ln 438-441) that would provide useful insights into a poorly understood\nvariability of atmospheric hydrogen.\nResponse: Thank you for this suggestion. We have now added an analysis of the sub-\ndecadal variability with the revised water-stress threshold (new Fig. S5). Notably, a\nlower moisture threshold has a stronger effect on vd(H ) on longer/interdecadal\n2\ntimescales rather than short-term/interannual timescales (L454-456).\nSigned cordially,\nAlex Tardito Chaudhri\nMinor comments:\n(ln 39) suggestion: these water potentials could be contextualised, e.g. ln 354,535 -3\nMPa is mentioned as the wilting point for semi-arid plants.\nResponse: Revised the sentence as: “We report H2 oxidizer activity down to –70 to –\nfor microbial activity and are much drier than assumed in global simulations of H2.” (L\n35-38).\n(ln 69-70) different estimates vary from 60s%-80s% so I’m not convinced that we can\nconfidently state that soil uptake is certainly over 70% of sink. E.g., based on\natmospheric hydrogen’s isotopic signature Rhee et al. 2006 get over 80%, but based on\nsome assumptions and what works in their models the Sand et al. 2023 estimate c.70%\nbut with some models less than that.\nResponse: Agreed, we revised to between 60-80% (L 65).\n(ln 89-90) this mention of the ‘paucity of observational constraints’ is very important, and\nI am impressed that the authors seek to address this. Suggestion: however, there is a\nrelative abundance of measurements in temperate NH America, so is it worth\nhighlighting the particular paucity of soil flux measurements in (semi-)arid soils?\nResponse: Revised to “particularly in moisture-limited, arid, and semi-arid soils” (L 86).\n(ln 126,127,174) there is a lack of consistency in lat-lon reporting. For usability, I would\nsuggest reporting in degrees with decimals in both cases, and for decimal places is it\nimportant or accurate that a location is reported to a precision equivalent to one metre?\nResponse: We thank the reviewer for highlighting this inconsistency in lat-lon reporting.\nWe have revised the degrees in decimals, as suggested. The precision to five decimal\nplaces (~1 meter) is not critical, but it approximately reflects the spatial scale of\nsubsampling, as at each site, soil samples were collected from three sub-locations\nspaced approximately 1 meter apart (see Methods L 125, 127, and 128).\n(ln 126,127) reliability: are there issues with collecting samples in single locations in\nyour sample areas? I understand that in-situ flux chamber measurements show\nenormous spatial variability uptake within metres of each other (e.g. Cowen et al.\nEGUsphere preprint-2024).\nResponse: Spatial variability is indeed a major axis of variability, and we thank the\nreviewer for drawing attention to a methodological detail that was omitted from the\nprevious version. We have now clarified our sampling strategy to specify that at each\nlocation, three locations spaced approximately 1 m apart were sampled and combined\ninto a composite sample (L 128-133).\n(ln 154) hyphen formatted as minus sign.\nResponse: Fixed to “combusted weight” (L 156).\n(ln 196-198) suggest these rates would be clearer to understand in ppb/min/g (to check\n– sometimes units formatted gSoil, other times as g-Soil).\nResponse: Revised as suggested.\n(ln 198-200) is this as trivial as discussed here? Chamber uptake of hydrogen is seen to\noccur on very short timescales, please explain how this model of drawdown is robust.\nResponse: The signal in the decaying H2 concentration acts on a timescale that\ndepends on the relative ratio between available substrate (scales with head space\nvolume) and bacterial activity (soil volume and uptake rate). We specifically designed\nour experiments to employ a limited amount of soil volume that would allow us to easily\ndiscriminate against an exponential decay of the H2 concentration within tens of\nminutes. We stress that an exponential decay, in addition of being confirmed by\nexperimental results, has to be expected, as the sink is proportional to the H2\nconcentration present (VdC/dt=k C, leading to an exponential solution for C). Although\nsometimes a linear uptake is referred to in the literature, there is no evidence of such a\ndecay trend in the concentration, which would require an unrealistic constant sink,\nindependent of the concentration. Chamber uptake is indeed faster, especially since\nthe chamber volume includes a much larger quantity of soil (relative to chamber\nheadspace volume) than what we had in our incubations. Furthermore, our incubations\nalso isolated the biotic component of uptake by minimizing physical diffusive limitation\nas would be present in a chamber experiment in the field, so data from these two\nexperimental systems are not directly comparable. A first-order approximation was\ndeemed appropriate since the uptake curves generally showed an exponential decay\ntrend (Fig. S3).\n(ln 215-266) I indicate that these sections are beyond my expertise. Suggestion: is it\nworth commenting how widely established these methods to give context to readers\nwithout this expertise?\nResponse: We have added a sentence to clarify these methods are standard and\nwidely established (L 213-215).\n(ln 317-318) suggestion: considering your helpful phrasing about the intrinsic nature of\n\\Psi (ln 354-355,379-381), could the link be better explained here? Also, do your ideas\nin ln 550-553 not give some good justification for why you might want to make this link\nsooner?\nResponse: Thank you, we revised the original L317-318 as: “we then show that using\nthe soil water potential rather than soil moisture offers better quantitative insights on the\nshared water-stress limitations for HA-HOB across soil types, as water potential\nprovides a more intrinsic measure of water availability that is comparable across\ndifferent soil textures” (L 329-333).\n(Fig. 1) are the scattered points the same tests in each panel, can the triangles, circles,\nsquares in a also be used in b. The legend in b shouldn’t have the scatter marker lying\non a line.\nResponse: We apologize for any confusion. In response to this comment, as well as a\nsimilar comment from reviewers 2 and 3, we revised Fig. 1 to remove the shapes and\nuse only colors to differentiate between soil types.\n(ln 434-436) please see commentary above regarding quantifying sub-decadal\nvariability of uptake.\nResponse: We have added a new figure to compare sub-decadal variability (Fig. S5).\n(ln 477) typo ‘to be range from’\nResponse: corrected (L 500).\n(ln 484) indicating\nResponse: corrected (L 547).\n(ln 498-499) suggestion: is it worth commenting on how usual/unusual it is to find a\nnovel genus in such tests, e.g. is it convincing that it is a novel genus rather than a\nmeasurement error?\nResponse: It isn’t unusual to detect novel genera or even higher-level taxa in soil\nsamples, as a significant fraction of microbial diversity remains uncharacterized (often\ntermed the “microbial dark matter”). Taxonomic classification is not subject to typical\nmeasurement errors encountered in analytical methods but is based on DNA sequence\nidentity and comparison against established taxonomic similarity thresholds. Our\nclassification is based on well-accepted bioinformatic programs and reference\ndatabases (GTDB-tk), providing robust support for identifying a novel genus rather than\na technical artifact.\n(Fig. 3) Clarity: as each genus can only be in one phylum please can the legend be\nsimplified to list the genera under sub-headings for the phyla with the correct symbols\n(currently they all look like acidobacteriota in the legend and it is hard to determine\ncolours). Then can you omit the ‘p__’s and ‘g__’s? Is ‘Flgellum’ a misspelling and should ‘flagellin’ have a capital\nF?\nResponse: We appreciate these detailed suggestions. While the prefixes are standard\npractice in microbial ecology to clarify taxonomic rank and avoid ambiguity, we have\nrevised the figure to remove them in order to improve accessibility for a broader\naudience. We have also copy-edited the figure as suggested (Fig. 4).\n(ln 576-578) this might be true in the sub-tropics and tropics, and presumably also when\nyou integrate everywhere, but can be potentially misleading if soil moisture is not the\nprimary driver everywhere. In (23) and (26), h(T) has a steep gradient between 0 and 20\ndegC. (35) see that the observed hydrogen distribution can be explained where NH\nextra-tropics mostly respond to temperature seasonality.\nResponse: We appreciate this comment and interpret this as a suggestion to highlight\nthat moisture may not be the primary control on H uptake across biomes. We revised\n2\nthe sentence to “soil moisture influences both the microbial activity and the physical\ndiffusion of H₂ through the soil matrix to remain the major modulator of H soil uptake.”\nhttps://github.com/Linta-Reji/Reji2025_H2_SoilMoisture provides clear instructions and\ndata files are available. (I have searched through the respository but not have not run\nthe code in R).\nResponse: Thank you!\nReviewer #2 (Remarks to the Author):\nJournal-specific considerations\n- What are the noteworthy results?\nThis article is the first to systematically relate soil H2 uptake to water potential, and the\nauthors show that H2 oxidation by microbes occurs at some of the lowest water\npotentials of any known microbial process. The authors implement new understanding\nof the relationship of H2 uptake and soil water-potential into a global model, which\nshows that arid lands likely make a relatively larger contribution to the global H2 sink\n- Will the work be of significance to the field and related fields? How does it compare to\nthe established literature? If the work is not original, please provide relevant references.\nYes, this work represents a significant advancement to our understanding of the\nmoisture sensitivity of overwhelming soil sink for atmospheric H2. The field is limited in\ndata, and this systematic study that was well-designed to meet the needs for upscaling\nwith models makes an important contribution to our understanding of its major driver of\nvariability.\n- Does the work support the conclusions and claims, or is additional evidence needed?\nYes.\n- Are there any flaws in the data analysis, interpretation and conclusions? - Do these\nprohibit publication or require revision?\nWe suggest that the authors focus their analysis of metagenomes and\nmetatranscriptomes more squarely on the hydrogenase genes and their expression\nrather than predominantly on MAGs (see Major Comments below). The analysis of\nMAGs could be extended to better understand how the organisms are physiologically\nresponding to moisture limitation. There are also interpretations related to SOC that are\nunclear. The authors could revise based on the feedback to make the paper suitable for\npublication.\nResponse: We thank the reviewer for their work and appreciate these suggestions and\nhave revised the manuscript as outlined in response to specific comments below.\n- Is the methodology sound? Does the work meet the expected standards in your field?\nYes.\n- Is there enough detail provided in the methods for the work to be reproduced?\nNo, but the missing points are minor and could be added in a revision.\nSummary\nReji et al., demonstrate that H2 oxidation occurs at some of the lowest water potentials\nknown for microbial metabolism and revise H2 sink models based on these results. The\nauthors integrate these findings into a global model and show that arid regions\ncontribute disproportionately to the global H2 sink, which has significant implications for\nclimate-relevant H2 cycling. A key strength of the paper is the scaling of measurements\nfrom the microbe to global scales and integration of lab-based and modeling\napproaches. The determination of a new, lower water potential threshold is also a\nnotable contribution. A weakness of the paper as presented is its interpretation of\nmetagenomic and metatranscriptomic data is underdeveloped, with an overreliance on\nMAG-level analyses and limited attention to gene-specific information (hydrogenase and\notherwise). Methodological clarity is also somewhat lacking, particularly in how omics\nreplicates were handled (particularly pooling of replicates), and the global modeling\nresults would benefit from clearer explanation findings that appear somewhat\ncounterintuitive in the discussion. Despite these issues, the core dataset and modeling\nframework are strong, and the paper makes a substantial contribution to the field. We\nrecommend major revisions with a special focus to strengthen the omics interpretation\nand clarify key methodological and conceptual points.\nMajor comments\n1) A more thorough exploration of the omics data\nIt sounds like DNA/RNA was extracted from individual forest replicates but from pooled\nmeadow and pine barrens soil. It needs to be explicitly stated in the methods how\nDNA/RNA were pooled before sequencing and whether any replicates (e.g., for the\nforest) were retained. It doesn’t appear that they were, which means that all\ndownstream analyses cannot include replicate-based statistical tests. Please discuss\nhow the lack of statistical evaluation limits the interpretability of the results. There are\nerror bars on Figure S6, but these appear to be internally derived from the stdev on the\nmean of n=2 genes within a given metagenome.\nResponse: Thank you for these comments. As noted in the original Methods (Lines\n218–222 of the previous version), DNA and RNA from the meadow soil and Pine\nBarrens sand were pooled prior to sequencing to ensure sufficient nucleic acid yield for\nmetagenome and metatranscriptome sequencing. Forest soil samples, by contrast,\nyielded sufficient nucleic acids from individual replicates, so one replicate each per\nmoisture level were sequenced separately. We have now re-stated this information in\nResults (L 485-487), in addition to in Methods (L219-223), to improve transparency. We\nare also including a new table (Table S3) that summarizes the extraction strategy.\nWe acknowledge that pooling of samples prior to extraction/sequencing precludes\nreplicate-based statistics, and accordingly, statistical tests were not applied to these\ndatasets. We believe the general trends we see across moisture levels are still robust\n(similar relative abundances of high-affinity H2ases across moisture levels in each soil\nas shown in Fig. S7; and moisture-dependent expression of H2ase subclusters as\nshown in the new Fig. 3). The relative abundances are also comparable to previous\nstudies in forest soils (Lines 498-501). The reviewers are correct that the error bars in\nFig. S6 (now Fig. S7) are not based on replicate-based statistics. While this information\nwas included in the original caption, we apologize if it was unclear. We have now added\na new sentence to the figure caption to explicitly state that the error bars do not\nrepresent biological replicates (L 117-118, SI).\nA primary concern is that the majority of the analysis of the genomic data are not\ndirectly related to the activity of the hydrogenase genes but are more focused on overall\nmeasures for the MAGs. Could the analysis be improved by implementing a moisture-\nbased analysis of any of the following suggestions?\n(i)Could the expression levels of Hase in the entire sample, including unbinned contigs,\nbe plotted to show overall community responses (like Fig. S6 but for expression)?\nResponse: This is a good suggestion—we have now added a new figure (Fig. 3) on\noverall HA-H2ase expression levels, in addition to MAG-based analyses.\n(ii)Instead of showing the expression of the MAGs as a whole (which needs to be\ndefined more clearly in the methods exactly what that means and how it is determined),\ncould Fig. 3b show the patterns in expression of the Hase in those MAGs?\nResponse: Thank you for this suggestion. Panel b now shows relative expression of\nthe hydrogenases in these MAGs (new Fig. 4).\nRegarding clarity on methods: Panel b originally showed the relative transcriptional\nactivity of the MAGs—which essentially translates to the relative number of RNA reads\nthat are mapped to each MAG. These were estimated by using standard methods as\ndescribed in the original Methods (L251-257 in the original manuscript), which involved\nmapping metatranscriptomic reads against each MAG using the program Bowtie2, and\nthen normalizing the counts of mapped reads as “transcripts per million” (L268-272). We\nagree that showing the expression of HA-H2ases is more relevant to the story, and\ntherefore, the figure has been revised as such (Fig. 4).\n(iii)The paper would benefit from an exploration of comparisons between transcript\nlevels of the H2 oxidizing genes and the other pathways of interest. Could the relative\nexpression of Hase vs other genes including the osmoprotectant genes (to support\nstatements in L591) and C-cycling genes (L509-515) in Fig. 3b be evaluated on a per-\nMAG basis?\nResponse: We appreciate this thoughtful suggestion; however, we do not think this\ncomparison is essential for the interpretations presented. There is no expectation of\ncoordinated expression among these genes; rather, the collective presence of these\npathways supports the interpretation that these organisms occupy an oligotrophic niche.\nHigh-affinity hydrogen oxidation is among the several stress adaptation strategies that\nthese organisms appear to possess.\n(iv) Overall, the patterns in Hase MAG abundance and expression were not sufficiently\ndiscussed. Given that such a high proportion of organisms appear to encode for these\nHases (Fig. S6), it is unclear how much can be derived from the pathway represented in\nother 24 MAGs recovered without Hases. To support Fig. 3a, we think it's necessary to\nnot only look at those bins which contain the potential for H2 oxidation, but also those\nother MAGs that don't. It would support claims about the functional roles of H2\nmetabolizers through comparison with the general microbial community or perhaps\nphylogenetically similar organisms that do not encode the H2 metabolism trait.\nResponse: Thank you for these suggestions. To panel (b) (now revised Fig. 4), we\nhave now added an additional feature that summarizes the prevalence of a given\npathway group in the non-HOB MAGs.\n(v)The relationship of these findings could be plotted versus water potential to more\nclearly evaluate their role in driving H2 oxidation, as you nicely show in Figure 1b.\nResponse: Thank you for this suggestion. Since the relationship between microbial\nomics results and water saturation/potential is qualitative, we think plotting these against\nwater potential as opposed to saturation would offer the same results.\n2) Relating H2 oxidation to soil carbon\nThe discussion of the ecological strategies for H2 oxidizers in low versus high C soils\nwould benefit from additional clarification and organization–especially to help explain\napparent contradictions. The first part of the discussion in L580 on carbon-rich soils\nappears to conflict with the later part in L584 on oligotrophic soil. If the H2 uptake per\nunit SOC is higher in the non-forest soils, how do we come to the conclusion that there\nis a higher reliance of the microbial community on alternative energy sources such as\nH2 in the forest soil? It could help to define whether ‘higher reliance’ means as a\ncommunity average or a per cell average? Could the authors add data from other\npapers to Fig. S4b to support the linear model. With three data points distributed, it is\neasy to get a high R2 and assume a linear form.\nResponse: We thank the reviewer for drawing attention to this. To clarify, when we refer\nto an ‘oligotrophic niche,’ we mean the metabolic lifestyle of these microbes—adapted\nto utilize low-resource substrates—rather than the carbon content of the soil itself (i.e.,\n“oligotrophic niche” does not equate to “oligotrophic soil”). These HA-HOB are\nmixotrophic, capable of oxidizing H₂ even in carbon-rich soils where competition for\norganic carbon may be intense. We have revised the text to make this distinction clearer\n(L 659-661).\nThe reviewers are also right to point out that a linear model is poorly supported with only\nthree data points. We acknowledge this limitation, which is why simulations with SOC\nmodulation are provided as supplementary figures. Such linear relationships have been\nobserved in prior studies (e.g., Hou et al., 2025,\nhttps://doi.org/10.1016/j.soilbio.2025.109831; Popelier at al., 1985, Plant and Soil 85, 85\n– 96). A key limitation in incorporating data from other papers to our model though is the\nfact that the rates obtained in previous work are not directly comparable to our\nmeasurements (diffusive limitation likely present in previous work). We also understand\nmore complex parameterizations involving soil carbon are being developed, and\nemphasize the need to better unravel the mechanistic links between SOC and H2\noxidation rates (L 644-646).\n3) Clarification of global projections for deposition velocity\nThe results and discussion on the change of deposition velocity globally require\nadditional clarification (Fig. 2). Specifically, the paper does not explain why lowering the\nwater potential threshold (e.g., from -3 to -100 Mpa) would decrease deposition velocity\nin continental regions. It is unclear whether this somehow shifts the model relationships\n(given by equations in this MS and Fig. 2 in Bertagni et al., 2021) or through some other\ninteraction. Physically, what could cause microbes to be able to access H2 at even\nlower moisture levels but reduce the deposition velocity of H2 overall? We understand\nthat Fig. 2c is compositional (%; this figure would be clearer if plotting the absolute\nvalues instead of percentage data) so the decrease in continental could just be at the\nexpense of the desert increasing. But continental values for vdep are also decreasing in\nan absolute sense in Fig in 2b vs 2a, and this is not sufficiently explained. Does this\nnew formulation not only allow more uptake at low moisture levels but also temper the\namount of uptake at higher moisture levels too? Along these lines, the implications for\nthe projected change in vdep over time should be discussed more thoroughly.\nResponse: Thank you for drawing attention to this. As detailed in the Methods section,\nvd(H2) is optimized for each configuration, such that the global deposition average vd(H2)\nis 0.09 mm/s for year 2010. Thus, our focus is primarily on the relative contribution of\ndifferent climate zones to the H2 sink. In particular, a lower water potential threshold for\nHA-HOB makes more land available in dry regions, which has to be compensated by a\nreduction in vd(H2) in regions that are generally not limited by dry conditions (e.g.,\ncontinental regions).\nMinor comments\nWe understand there are trade-offs in selecting the timeline of the experiment. Please\ndiscuss the impact of the duration of the incubations relative to the possible growth rate\nof these organisms in the manuscript.\nResponse: We agree that the duration of incubation is an important consideration when\nmeasuring microbial process rates. Our incubations lasted 5–6 days, which we selected\nto ensure stable measurement of H₂ oxidation potential while minimizing large\ncommunity shifts. Additionally, soil bacterial growth rates are generally low, with in situ\ndoubling times often on the order of days to weeks—a recent analysis showed average\ngeneration times of 14 to 45 d, with a range of 4 to 402 days (Caro et al., 2023). Even\nthese estimates reflect optimal laboratory conditions rather than field-relevant rates.\nThus, while we cannot rule out the possibility of some microbial growth during the\nincubation, we expect that the measured H₂ oxidation rates primarily reflect the\nmetabolic potential of the original community rather than substantial enrichment of H₂\noxidizers during incubation.\nCaro, T. A., et al., 2023, PNAS, 120 (16) e2211625120.\nhttps://doi.org/10.1073/pnas.2211625120\nTemperate forests and grasslands are some of the best represented by current data,\nand the authors should explain why these sites were chosen. How could the fact that\nyour sites are all acidic influence your results and conclusions?\nResponse: We agree with the reviewer that temperate forests and grasslands are\nrelatively better studied biomes in prior H₂ oxidation research. Our site selection,\nhowever, was guided by the study’s central question—identifying the soil moisture\nthreshold for high-affinity hydrogen oxidizers—rather than by an aim to maximize\ncoverage of global soil types. All of our sites are acidic, which may influence community\ncomposition and activity. We have added a statement in the Discussion acknowledging\nthe need to characterize more soil types (L 688-690).\nL44: Define pp in the abstract.\nResponse: Revised.\nLines 75-76: “The global warming potential of H2” should likely also indicate that this is\n“indirect”.\nResponse: Revised as suggested (L 72).\nLines 87-91: Might be beneficial to expand a bit on the work that has been done rather\nthan essentially just saying it has lead to inaccurate estimations.\nResponse: Thank you for this suggestion. We have revised this sentence to clarify that\nlimitations in prior work primarily stem from sparse data in moisture-limited, arid, and\nsemi-arid soils. The revised sentence now reads: “Due to the paucity of observational\nconstraints on soil uptake, particularly in moisture-limited, arid, and semi-arid soils,\nthese parameterizations result in inaccurate estimates of the seasonality and spatial\nvariability of uptake” (L 85-88).\nL117: Ending the introduction with a statement on what the big-picture importance of the\nwork could be would be more compelling than ending by listing another method.\nResponse: resolved (L 113-115).\nL126: Since you have two forest sites, could you use more specific shorthand names\nrather than forest vs PB?\nResponse: We thank the reviewers for this suggestion. While we have two forested\nsites, these are not equivalent at all—the first is a typical deciduous temperate forest\nwhile the Pine Barrens represents a highly nutrient-poor sandy ecosystem distinct from\ntypical forests. We therefore prefer to retain the terms ‘forest’ and ‘Pine Barrens’\nthroughout the text and figures to highlight this ecological contrast, while providing site\ndetails in the Methods for clarity.\nLines 129-130: “At each location, soil samples were collected from the top ~10 cm, after\nremoving the organic layer, if present.” Would we expect the organic layer to not impact\nH2 diffusion in natural systems? Both by abiotic (diffusion limitation) and biotic (H2\noxidation) mechanisms.\nResponse: Indeed, the organic (litter) layer potentially has a significant influence on H2\noxidation. However, our goal here is to not assess natural variability in H2 oxidation\nrates, rather to isolate the effect of soil moisture on the biotic process within the mineral\nsoil. Including the organic layer would have introduced additional variability due to\ndifferences in litter composition, depth, and moisture-holding capacity among sites,\nmaking it more difficult to disentangle the specific role of soil moisture on HA-HOB\nactivity.\nL200: Please provide full details for your H2 oxidation rate calculation. Please give a\nrange of estimated fluxes for comparison to other studies. Specify what you used as the\ninitial H2 concentration to do so and why (one concentration for all samples or did it vary\nbetween samples as initial H2 measurement)?\nResponse: All the measured concentrations and the code we used to determine\noxidation rates are included in the specified GitHub repository. As specified in the\nMethods, H2 oxidation was modeled as a first-order decay process, and the rate\nconstants (min-1) were used a proxy for oxidation rates. The initial H2 concentration\nvaried across incubations as we are starting the experiment with ambient H2 levels\neach time. The calculated rate constants therefore reflect the removal rate at these\nnear-ambient concentrations.\nTo emphasize this further, we have added a new sentence to Methods: “Initial H\n2\nconcentrations matched the corresponding ambient H levels measured for each\n2\nexperiment.” (L 195-197).\nL207-209: Give soil mass to be consistent with what you stated you added to the jars.\nHow much soil was used for the gravimetric moisture analysis? Was it enough to\ndetermine significant shifts in moisture? Describe any limitations in the water potential\nmeasurement for such a small amount of soil.\nResponse: We apologize for any confusion/lack of clarity and have now specified in the\nMethods the exact amounts of soil used for gravimetric moisture determination. For\nwater potential measurements, the WP4C sample cups are filled hallway for analysis as\nthe protocol requires, which corresponds to ~1-2 mL of soil (volume and weight varies\nwith soil type/texture). This is the standard sample amount recommended by the\nmanufacturer for WP4C measurements. Although fine-scale heterogeneity in soil\nstructure/moisture could indeed affect both gravimetric and WP measurements, our\nvalues were generally consistent among replicates within each soil-moisture level\ncombination.\nL228: Is this the right table? It is not clear from this combination of tables what the\nmoisture levels were for the samples sequenced, unless it is all the samples in Table\nS1. Please clarify in the caption.\nResponse: Added a new SI table summarizing extraction results. (Table S3)\nL340: Fastest microbial H2 uptake\nResponse: Revised as suggested (L355-356).\nL363: It would be interesting to discuss these observations of the impact of recently\nadded water to H2 oxidation in light of the intermittent H2 uptake concepts presented in\nBertagni et al., 2021.\nResponse: Thank you for this insightful comment. Our results indeed suggest that the\nwater-stress threshold for soil bacteria may be lower than previously assumed. This\nimplies that intermittent H₂ uptake could still occur, but with a lower moisture\nthreshold—potentially one that can be reached even with morning dew events. This is a\nparticularly interesting aspect that would warrant dedicated field studies or controlled\ndry–wetting experiments, which, however, fall beyond the scope of the present work.\nL463: We have seen H2ase used more frequently (10.1111/j.1574-\n6976.2001.tb00587.x. ) than Hase\nResponse: revised as suggested.\nL484: Check grammar\nResponse: Corrected\nFig. 1: ‘Faded’ colors are pretty hard to distinguish. Could you visualize in a more\nspecific way? It’s not clear initially that the AZ soils are represented by a range of bars.\nWe think the problem is that they are located up near all the other legends. Could you\ndifferentiate the legends from the ranges, perhaps with a box around the legends? We\ndon’t think they need to have separate colors since there are no points to look for in the\nplot–they just add confusion. Clarify whether or not these bars relate to the y axis as\nwell. What does ‘/porosity’ mean in the caption? This should be placed in methods\ninstead: Saturation levels (%) were estimated as volumetric moisture content/porosity.\nResponse:\nWe made several changes to Fig. 1 to improve visual clarity:\n1. The shaded points are now a much lighter shade.\n2. We added boxes around the legends\n3. We moved the AZ soil water potential ranges to below the horizontal axis to\n(a) differentiate them better from the AZ soil ranges and (b) clarify that they\nare not related to the vertical axis.\n4. In Fig. 1a, we retained the point colors, but removed the varying shapes,\nsince they were redundant.\n5. We removed the sentence about saturation and porosity, as suggested (this is\nalready discussed in Methods).\nFig. 2: Is there a way that you could annotate Fig. 2d to indicate what a positive vs\nnegative change means? We are assuming positive is an increase in vd in the later\nperiod vs the earlier period, but we wonder if you could annotate that to be more clear?\nAt first, we were confused whether c and d corresponded with a and b, and others might\nbe too. It might also be useful to adjust the axis labels on the bar charts so that they are\ncentered beneath the bears rather than far to the side of them.\nResponse: We have revised Fig. 2 to address these comments.\nFig. 3b: Check spelling “Flgellum”\nResponse: Revised.\nTable S1: Indicate if water content is gravimetric % saturation.\nResponse: Clarified as suggested.\nFig. S4: H2 oxidation rates increase with\nResponse: Corrected.\nReviewer #2 (Remarks on code availability):\nWe were unable to access the code. The github link did not connect to us to a\nfunctioning page.\nResponse: Thank you for pointing this out. The first reviewer was able to access the\ncode, so we are not sure what went wrong in this case. The code and associated data\nfiles should be available at the link provided in the Data Availability Statement.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nResponse: Thank you!"}]

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