Abstract The grassland biome is an important sink for atmospheric methane (CH4), a major greenhouse gas. There is considerable uncertainty in the grassland CH4 sink capacity due to diverse environmental gradients in which grasslands occur, and many environmental conditions can affect abiotic (e.g., CH4 diffusivity into soils) and biotic (e.g., methanotrophy) factors that determine spatial and temporal CH4 dynamics. We investigated the relative importance of a soil's gas diffusivity versus net methanotroph activity in 22 field plots in seven sites distributed across the US Great Plains by making approximately biweekly measures during the growing seasons over 3 years. We quantified net methanotroph activity and diffusivity by using an approach combining a gas tracer, chamber headspace measurements, and a mathematical model. At each plot, we also measured environmental characteristics, including water‐filled pore space (WFPS), soil temperature, and inorganic nitrogen contents, and examined the relative importance of these for controlling diffusivity and net methanotroph activity. At most of the plots across the seven sites, CH4 uptake rates were consistently greatest when WFPS was intermediate at the plot level. Our results show that variation in net methanotroph activity was more important than diffusivity in explaining temporal variations in net CH4 uptake, but the two factors were equally important for driving spatial variation across the seven sites. WFPS was a significant predictor for diffusivity only in plots with sandy soils. WFPS was the most important control on net methanotroph activity, with net methanotroph activity showing a parabolic response to WFPS (concave down), and the shape of this response differed significantly among sites. Moreover, we found that the WFPS level at peak net methanotroph activity was strongly correlated with the mean annual precipitation of the site. These results suggest that the local precipitation regime determines unique sensitivity of CH4 uptake rates to soil moisture. Our findings indicate that grassland CH4 uptake may be predicted using local soil water conditions. More variable soil moisture, potentially induced through predicted future extremes of rainfall and drought, could reduce grassland CH4 sink capacity in the future.

This article is a Commentary on the Virtual Issue ‘Methane emissions from tree stems – current knowledge and challenges’ that includes the following papers: Barba et al . (2019), Bréchet et al . (2021), Covey & Megonigal (2019), Feng et al . (2022), Flanagan et al . (2021), Jeffrey et al . (2019, 2021, 2023), Kohl et al . (2019), Machacova et al . (2021a,b, 2023), Megonigal et al . (2020), Pangala et al . (2013, 2014), Pitz & Megonigal (2017), Plain et al . (2019), Putkinen et al . (2021), Sjögersten et al . (2020), Takahashi et al . (2022), Tenhovirta et al . (2022), Wang et al . (2016), and Yip et al . (2018). Access the Virtual Issue at www.newphytologist.com/virtualissues .

Tree stems can be a major source of CH4, altering the impact forests have on atmospheric radiative forcing. In the tropics stem flux monitoring has been limited, nevertheless, available field studies have observed high variability of stem CH4 flux rates between species and individuals and over the surface of individual tree stems. To evaluate the sources of variation and controls of CH4 stem fluxes, methods supporting large sampling campaigns are needed. We designed a portable, flexible, easily installed chamber that produces replicable flux measurements across broad species diversity. The chamber creates a strong seal on a range of stem sizes and surface roughnesses and extends radially, integrating surface flux variation so that small hot spots are not missed. Working in forested Amazonian peatlands, we used this chamber to measure stem fluxes on a variety of palms and trees with stems ranging from 10 cm to 85 cm DBH, both rough and smooth barked species. We compared a non-steady state, headspace recirculation method to a steady-state flow-through method and found the flow-through method likely yields more accurate estimates for high emissions. Our novel stem flux measurements from Amazonian peatlands reveal that stem CH4 emissions of the dominant palm (M. flexuosa) contrast markedly against dominant hardwood species, with palms emitting CH4 at much higher rates (up to 84 mg-C m(-2) h(-1) at 0.23 m stem height) that compare to the highest published rates of tree stem emission. Flux rates from M. flexuosa demonstrated exponential decay with stem height, rates at 0.5 m were 3 to 10 times higher than at 1.4 m. This pattern underscores the importance of quantifying CH4 flux rates over the lower 2 m of stem in order to accurately quantify stem CH4 fluxes. Considering their broad distribution and high density in Amazonian peatlands, M. flexuosa stems may be a major source of atmospheric CH4.

Summary Trees are sources, sinks, and conduits for gas exchange between the atmosphere and soil, and effectively link these terrestrial realms in a soil–plant–atmosphere continuum. We demonstrated that naturally produced radon‐222 (222Rn) gas has the potential to disentangle the biotic and physical processes that regulate gas transfer between soils or plants and the atmosphere in field settings where exogenous tracer applications are challenging. Patterns in stem radon emissions across tree species, seasons, and diurnal periods suggest that plant transport of soil gases is controlled by plant hydraulics, whether by diffusion or mass flow via transpiration. We establish for the first time that trees emit soil gases during the night when transpiration rates are negligible, suggesting that axial diffusion is an important and understudied mechanism of plant and soil gas transmission.

Tree stems from wetland, floodplain and upland forests can produce and emit methane (CH4). Tree CH4 stem emissions have high spatial and temporal variability, but there is no consensus on the biophysical mechanisms that drive stem CH4 production and emissions. Here, we summarize up to 30 opportunities and challenges for stem CH4 emissions research, which, when addressed, will improve estimates of the magnitudes, patterns and drivers of CH4 emissions and trace their potential origin. We identified the need: (1) for both long-term, high-frequency measurements of stem CH4 emissions to understand the fine-scale processes, alongside rapid large-scale measurements designed to understand the variability across individuals, species and ecosystems; (2) to identify microorganisms and biogeochemical pathways associated with CH4 production; and (3) to develop a mechanistic model including passive and active transport of CH4 from the soil-tree-atmosphere continuum. Addressing these challenges will help to constrain the magnitudes and patterns of CH4 emissions, and allow for the integration of pathways and mechanisms of CH4 production and emissions into process-based models. These advances will facilitate the upscaling of stem CH4 emissions to the ecosystem level and quantify the role of stem CH4 emissions for the local to global CH4 budget.

Global Change BiologyVolume 25, Issue 8 p. e6-e8 RESPONSE TO THE EDITORFree Access Building bottom-up aggregate-based models (ABMs) in soil systems with a view of aggregates as biogeochemical reactors Bin Wang, Corresponding Author Bin Wang wbwenwu@gmail.com bw8my@virginia.edu orcid.org/0000-0003-0453-458X Department of Ecology and Evolutionary Biology, University of California, Irvine, California Correspondence Bin Wang, Department of Ecology and Evolutionary Biology, University of California, Irvine, 321 Steinhaus Hall, Irvine, CA 92697-2525, USA. Email: wbwenwu@gmail.com; bw8my@virginia.eduSearch for more papers by this authorPaul E. Brewer, Paul E. Brewer Smithsonian Environmental Research Center, Edgewater, MarylandSearch for more papers by this authorHerman H. Shugart, Herman H. Shugart Department of Environmental Sciences, University of Virginia, Charlottesville, VirginiaSearch for more papers by this authorManuel T. Lerdau, Manuel T. Lerdau Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia Department of Biology, University of Virginia, Charlottesville, VirginiaSearch for more papers by this authorSteven D. Allison, Steven D. Allison orcid.org/0000-0003-4629-7842 Department of Ecology and Evolutionary Biology, University of California, Irvine, California Department of Earth System Science, University of California, Irvine, CaliforniaSearch for more papers by this author Bin Wang, Corresponding Author Bin Wang wbwenwu@gmail.com bw8my@virginia.edu orcid.org/0000-0003-0453-458X Department of Ecology and Evolutionary Biology, University of California, Irvine, California Correspondence Bin Wang, Department of Ecology and Evolutionary Biology, University of California, Irvine, 321 Steinhaus Hall, Irvine, CA 92697-2525, USA. Email: wbwenwu@gmail.com; bw8my@virginia.eduSearch for more papers by this authorPaul E. Brewer, Paul E. Brewer Smithsonian Environmental Research Center, Edgewater, MarylandSearch for more papers by this authorHerman H. Shugart, Herman H. Shugart Department of Environmental Sciences, University of Virginia, Charlottesville, VirginiaSearch for more papers by this authorManuel T. Lerdau, Manuel T. Lerdau Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia Department of Biology, University of Virginia, Charlottesville, VirginiaSearch for more papers by this authorSteven D. Allison, Steven D. Allison orcid.org/0000-0003-4629-7842 Department of Ecology and Evolutionary Biology, University of California, Irvine, California Department of Earth System Science, University of California, Irvine, CaliforniaSearch for more papers by this author First published: 14 May 2019 https://doi.org/10.1111/gcb.14684Citations: 7AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat In our recent article in Global Change Biology (Wang, Brewer, Shugart, Lerdau, & Allison, 2019), we proposed to develop aggregate-based models (ABMs) based on a view of soil aggregates as biogeochemical reactors in the context of soil heterogeneity. Using a bottom-up philosophy, we argued for developing ABMs based on a systematic and dynamic view of soils as a constellation of aggregate reactors of different sizes. We envision that these ABMs offer the potential to bring new mechanistic perspectives into soil system modelling. In a letter to the editor by Kravchenko et al. (2019) an alternative opinion is articulated, and we appreciate the authors' thoughtful comments. One element of this opinion is that soil system functioning is not a simple sum of soil constituents—we agree with this statement. Another objection from Kravchenko et al. is primarily based on indeterminacies of size and boundary conditions of aggregate reactors. We also agree that these limitations are important, and we began to address them in Section 6 of our article (Wang et al., 2019). However, we believe that these challenges arising from traditional soil fractionation techniques do not necessarily dilute our confidence in developing ABMs as a prognostic framework that integrates soil processes from the bottom-up. We are grateful to have the opportunity here to further clarify our view and share new thoughts on it. A bottom-up modelling approach is the 'Holy Grail' of soil system modelling that has been difficult to achieve because of the soil's opaque and heterogeneous nature. In contrast, there has been a successful infusion of this modelling philosophy into such fields as ecology, sociology, economics, physics and others (e.g. Auyang, 1998; Shugart et al., 2018). In soil science, aggregates reflect soil system development ('succession'). Aggregates of different sizes form and collapse constantly during aggregate 'ontogeny', defined by aggregate turnover/stability, while interacting with many endogenous and exogenous factors. In this context we propose that aggregates, as physically distinct units embedded in the complex soil matrix, can be viewed as biogeochemical reactors, in which biogeochemical reactions actively transpire and across which soil macropores bridge interactions. By explicitly simulating aggregate reactors of different sizes along with their interactions, soil system functioning can be quantified as an emergent property of finer scale processes. This bottom-up modelling philosophy reflects how we understand soil system composition, structure, function and dynamics. From this perspective, we firmly believe that viewing soil aggregates as physically independent units is a way forward for understanding soil system functioning. In building ABMs, aggregate separation techniques and even artificial aggregates have played and will continue to play a pivotal role in gaining theoretical understanding of aggregate reactors and their size-dependent relationships with various factors (e.g. Upton, Bach, & Hofmockel, 2019; Path 1 in Figure 1). Aggregate-based approaches can offer an advantage of measurability relative to current soil carbon models such as CENTURY for which the simulated carbon pools cannot be measured directly (Parton, 1996). Although building ABMs based on laboratory-derived aggregate sizes is a good starting point, Kravchenko et al. are legitimately concerned about indeterminacy in real soils. Still, in situ observations of size distributions of aggregate reactors are possible via tomography techniques (e.g. X-Ray CT for bulk soil characterization [Schlüter, Zawallich, Vogel, & Dörsch, 2019] and SEM for finer structure [Smith, 2008]) (Path 2 in Figure 1). Even more promising are deep learning techniques for image recognition that can accelerate the retrieval of rich soil structural information from high-resolution soil images derived from these tomography techniques (Reichstein et al., 2019). Therefore, knowledge from traditional soil fractionations and new data on soil structure powered by machine learning can inform ABM development with aggregate reactors as fundamental units (Figure 1). Figure 1Open in figure viewerPowerPoint Framework for building aggregate-based models (ABMs) in soil systems. Theories built upon traditional soil fractionation and even artificial aggregates (Path 1) and the size distribution of aggregate reactors derived from tomography powered by machine learning (Path 2) would inform development of ABMs from the bottom-up. This theory can be further constrained by top–down measurements of intact soils through model-data assimilation (Path 3). PDF, probability density function Moreover, top–down constraints based on data from intact soils can further address shortcomings of the bottom-up approaches (Path 3 in Figure 1). For example, boundary conditions of aggregate reactors (dependent on interaggregate spaces or macropores) are hard to determine because of methodological challenges in conducting in situ measurements. Such a lack of in situ information will increase the parameter uncertainty of ABMs. This issue is analogous to the determination of abiotic environment conditions, such as light intensity, surrounding an individual tree crown in a diverse forest system, which, though still hard to measure explicitly, do not hinder explicit model development (e.g. Wang, Shugart, & Lerdau, 2017). Regarding aggregate reactors, one feasible and efficient approach would be to calibrate ABMs with data derived from intact soils (Kennedy & O'Hagan, 2001). Our original article, therefore, emphasized the utility of top–down experiments (Wang et al., 2019) as also stressed by Kravchenko et al. (2019). In summary, because they are mechanistically and structurally explicit, we argue that ABMs are a valuable tool for advancing soil system science (see a recent example by Ebrahimi & Or, 2018). Some of the key challenges facing ABMs can be addressed readily with a combination of theory-driven and data-driven approaches (Figure 1). We hope more researchers from soil science, ecology, data science and beyond will join in this discussion of developing bottom-up ABMs by viewing soil aggregates as relatively distinct units. We maintain that biogeochemical reactors are a useful concept for understanding soil functioning in the context of global environmental changes. ACKNOWLEDGEMENTS We thank Dr. Zan Gao, Dr. Jianhua Ma and Dr. Yunya Zhang (UVA Engineering), as well as Dr. Fulin Wang (UC Santa Barbara Engineering) for their inspirations to B. Wang on connecting tomography and machine learning to soil structure. REFERENCES Auyang, S. Y. (1998). Foundations of complex system theories in economics, evolutionary biology, and statistical physics. New York: Cambridge Univ. Press. Ebrahimi, A., & Or, D. (2018). On upscaling of soil microbial processes and biogeochemical fluxes from aggregates to landscapes. Journal of Geophysical Research: Biogeosciences, 123, 1526– 1547. https://doi.org/10.1029/2017JG004347 Kennedy, M. C., & O'Hagan, A. (2001). Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63, 425– 464. https://doi.org/10.1111/1467-9868.00294 Kravchenko, A., Otten, W., Garnier, P., Pot, V., & Baveye, P. C. (2019). Soil aggregates as biogeochemical reactors: Not a way forward in the research on soil–atmosphere exchange of greenhouse gases. Global Change Biology, https://doi.org/10.1111/gcb.14640 Parton, W. J. (1996) The CENTURY model. In D. S. Powlson, P. Smith, & J. U. Smith (Eds.), Evaluation of soil organic matter models. NATO ASI series (Series I: Global environmental change) (Vol. 38). Berlin, Heidelberg: Springer. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566, 195– 204. https://doi.org/10.1038/s41586-019-0912-1 Schlüter, S., Zawallich, J., Vogel, H.-J., & Dörsch, P. (2019). Physical constraints for respiration in microbial hotspots in soil and their importance for denitrification. Biogeosciences Discussions, 1– 31. https://doi.org/10.5194/bg-2019-2 Shugart, H. H., Wang, B., Fischer, R., Ma, J., Fang, J., Yan, X., … Armstrong, A. H. (2018). Gap models and their individual-based relatives in the assessment of the consequences of global change. Environmental Research Letters, 13, 033001. https://doi.org/10.1088/1748-9326/aaaacc Smith, D. J. (2008). Ultimate resolution in the electron microscope? Materials Today, 11, 30– 38. https://doi.org/10.1016/S1369-7021(09)70005-7 Upton, R. N., Bach, E. M., & Hofmockel, K. S. (2019). Spatio-temporal microbial community dynamics within soil aggregates. Soil Biology and Biochemistry, 132, 58– 68. https://doi.org/10.1016/j.soilbio.2019.01.016 Wang, B., Brewer, P. E., Shugart, H. H., Lerdau, M. T., & Allison, S. D. (2019). Soil aggregates as biogeochemical reactors and implications for soil–atmosphere exchange of greenhouse gases—A concept. Global Change Biology, 25, 373– 385. https://doi.org/10.1111/gcb.14515 Wang, B., Shugart, H. H., & Lerdau, M. T. (2017). An individual-based model of forest volatile organic compound emissions—UVAFME-VOC v1. 0. Ecological Modelling, 350, 69– 78. https://doi.org/10.1016/j.ecolmodel.2017.02.006 Citing Literature Volume25, Issue8August 2019Pages e6-e8 FiguresReferencesRelatedInformation

In our recent article in Global Change Biology (Wang et al., 2019), we proposed to develop aggregate-based models (ABMs) based on a view of soil aggregates as biogeochemical reactors in the context of soil heterogeneity. Using a bottom-up philosophy, we argued for developing ABMs based on a systematic and dynamic view of soils as a constellation of aggregate reactors of different sizes. We envision that these ABMs offer the potential to bring new mechanistic perspectives into soil system modelling. This article is protected by copyright. All rights reserved.

Anoxic microsites can alter the habitat of upland soils and host diverse anaerobic processes that affect green-house gas production, nitrogen dynamics, and biodiversity. Microsites that are methanogenic indicate deeply reducing conditions that may have especially strong impacts on soil function. However, there have not been controlled studies to determine the regulators of methanogenic microsite formation or persistence and most studies have been limited to tropical or high organic matter soils. We hypothesized that upland methanogenesis, as an indicator of anaerobic activity, is primarily affected by soil moisture and organic matter. To test this hypothesis, we examined relationships between soil properties, rates of methanogenesis, and biogeochemical responses in an incubation experiment that manipulated soil source (semi-arid and mesic ecosystems), agricultural practice (conventional, no-till, and organic), and moisture (10%-95% water-filled porespace) of intact soil cores. Methanogenesis was correlated with factors related to both increased O-2 demand (e.g., soil respiration) and decreased O-2 diffusion (e.g., water-filled porespace), and the relative importance of these different mechanisms changed over four months. While the highest rates of methanogenesis occurred above 75% water-filled porespace, we observed methanogenesis over the full range of soil moistures. These are the driest soils shown to host methanogenesis, outside of biological soil crusts. Cores from plots with organic amendments had the highest rates of methanogenesis. Comparisons of methanogenesis and N-cycling revealed new relationships in upland soils: stronger methanogenesis was associated with more soil NH4+ and higher N2O emissions but less NO3-, likely due to reduced conditions causing increased denitrification and/or decreased nitrification. Our findings show that upland methanogenesis can arise from either increased O-2 demand or decreased O-2 diffusion, similar to wetland ecosystems, and that the presence of anoxic microsites appears to alter N-cycling. The current paradigm is that upland anaerobicity is generally a minor or moisture-related event, but we demonstrate here that it can be persistent, occur across the full range of soil moisture, and may result in significant impacts on nutrient availability. These and other anaerobic impacts on soil function and biodiversity may occur over the entire landscape of temperate ecosystems.

Managing leaks in urban natural gas (NG) distribution systems is important for reducing methane emissions and costly waste. Mobile surveying technologies have emerged as a new tool for monitoring system integrity, but this new technology has not yet been widely adopted. Here, we establish the efficacy of mobile methane surveys for managing local NG distribution systems by evaluating their ability to detect and locate NG leaks and quantify their emissions. In two cities, three-quarters of leak indications from mobile surveys corresponded to NG leaks, but local distribution companies' field crews did not find most of these leaks, indicating that the national CH4 activity factor for leaks in local NG distribution pipelines is underestimated by a factor of 2.4. We found the median distance between mobile-estimated leak locations and actual leak locations was 19 m. A comparison of emission quantification methods (mobile-based, surface enclosure, and tracer ratio) found that the mobile method overestimated leak magnitude for the smallest leaks but accurately estimated size for the largest leaks that are responsible for the majority of total emissions. Across leak sizes, mobile methods adequately rank relative emission rates for repair prioritization, and they are easily deployed and offer efficient spatial coverage.

Soil-atmosphere exchange significantly influences the global atmospheric abundances of carbon dioxide (CO2 ), methane (CH4 ), and nitrous oxide (N2 O). These greenhouse gases (GHGs) have been extensively studied at the soil profile level and extrapolated to coarser scales (regional and global). However, finer scale studies of soil aggregation have not received much attention, even though elucidating the GHG activities at the full spectrum of scales rather than just coarse levels is essential for reducing the large uncertainties in the current atmospheric budgets of these gases. Through synthesizing relevant studies, we propose that aggregates, as relatively separate micro-environments embedded in a complex soil matrix, can be viewed as biogeochemical reactors of GHGs. Aggregate reactivity is determined by both aggregate size (which determines the reactor size) and the bulk soil environment including both biotic and abiotic factors (which further influence the reaction conditions). With a systematic, dynamic view of the soil system, implications of aggregate reactors for soil-atmosphere GHG exchange are determined by both an individual reactor's reactivity and dynamics in aggregate size distributions. Emerging evidence supports the contention that aggregate reactors significantly influence soil-atmosphere GHG exchange and may have global implications for carbon and nitrogen cycling. In the context of increasingly frequent and severe disturbances, we advocate more analyses of GHG activities at the aggregate scale. To complement data on aggregate reactors, we suggest developing bottom-up aggregate-based models (ABMs) that apply a trait-based approach and incorporate soil system heterogeneity.

Greenhouse gas (GHG) emissions from thawed permafrost are difficult to predict because they result from complex interactions between abiotic drivers and multiple, often competing, microbial metabolic processes. Our objective was to characterize mechanisms controlling methane (CH4) and carbon dioxide (CO2) production from permafrost. We simulated permafrost thaw for the length of one growing season (90 days) in oxic and anoxic treatments at 1 and 15 °C to stimulate aerobic and anaerobic respiration. We measured headspace CH4 and CO2 concentrations, as well as soil chemical and biological parameters (e.g. dissolved organic carbon (DOC) chemistry, microbial enzyme activity, N2O production, bacterial community structure), and applied an information theoretic approach and the Akaike information criterion to find the best explanation for mechanisms controlling GHG flux. In addition to temperature and redox status, CH4 production was explained by the relative abundance of methanogens, activity of non-methanogenic anaerobes, and substrate chemistry. Carbon dioxide production was explained by microbial community structure and chemistry of the DOC pool. We suggest that models of permafrost CO2 production are refined by a holistic view of the system, where the prokaryote community structure and detailed chemistry are considered. In contrast, although CH4 production is the result of many syntrophic interactions, these actions can be aggregated into a linear approach, where there is a single path of organic matter degradation and multiple conditions must be satisfied in order for methanogenesis to occur. This concept advances our mechanistic understanding of the processes governing anaerobic GHG flux, which is critical to understanding the impact the release of permafrost C will have on the global C cycle.

Methane is a potent greenhouse gas, and the uptake of methane by methanotrophic bacteria in oxic, well-drained soils is a key global sink. Field studies at the ecosystem scale have observed significant temporal and spatial variation in methane uptake rates, but there is considerable uncertainty about the roles of abiotic and biotic factors, including methanotroph community composition, in structuring these patterns. Here, we present an analysis of Michaelis Menten kinetics of methane uptake in soils collected from three North American temperate grassland sites of differing soil moisture regimes and their methanotroph community composition. The three sites were Konza Prairie in Kansas, Shortgrass Steppe in Colorado and Sevilleta in New Mexico with mean annual precipitation of 835, 320 and 244 mm, respectively. Michaelis-Menten kinetics and methanotroph community were assessed via lab incubation and pmoA-based phylogeny, respectively. Across the precipitation gradient we observed distinct variation in Michaelis-Menten kinetics and methanotroph community composition. Both K-M and V-Max values of the Michaelis-Menten kinetics followed the trend of the mean annual precipitation (Konza Prairie > Shortgrass Steppe > Sevilleta). The observed six methanotroph clades were all within the gamma-proteobacteria division, and included two novel clades found in Shortgrass Steppe and Sevilleta. The methanotroph communities were dominated by Methylococcus spp, JR2 Glade, and USC gamma, in Konza Prairie, Shortgrass Steppe and Sevilleta soils, respectively. The distinct differences in the community composition among the three sites may help explain the functional variation of upland methanotrophy observed in K-M. Taken together, the coincident differences in Michaelis-Menten kinetics we observed suggest that methanotroph community composition can be important for CH4 uptake in controlled environments, potentially playing a role in the variation in methane uptake in the fields. (C) 2015 Elsevier Ltd. All rights reserved.