Global Soil Research Articles

Global Optimization of Soil Texture Maps From Satellite‐Observed Soil Moisture Drydowns and Its Implementation in Noah‐MP Land Surface Model

AbstractSoil moisture (SM) plays an important role in regulating regional weather and climate. However, the simulations of SM in current land surface models (LSMs) contain large biases and model spreads. One primary reason contributing to such model biases could be the misrepresentation of soil texture in LSMs, since current available large‐scale soil texture data are often generated from extrapolation algorithm based on a scarce number of in‐situ geological measurements. Fortunately, recent advancements in satellite technology provide a unique opportunity to constrain the soil texture data sets by introducing observed information at large spatial scales. Here, two major soil texture baseline data sets (Global Soil Data sets for Earth system science, GSDE and Harmonized World Soil Data from Food and Agriculture Organization, HWSD) are optimized with satellite‐estimated soil hydraulic parameters. The optimized soil maps show increased (decreased) sand (clay) content over arid regions. The soil organic carbon (SOC) content increases globally especially over regions with dense vegetation cover. The optimized soil texture data sets are then used to run simulations in one example LSM, that is, Noah LSM with Multiple Parameters. Results show that the simulated SM with satellite‐optimized soil texture maps is improved at both grid and in‐situ scales. Intercase comparison analyses show the SM improvement differs between simulations using different soil maps and soil hydraulic schemes. Our results highlight the importance of incorporating observation‐oriented calibration on soil texture in current LSMs. This study also joins the call for a better soil profile representation in the next generation of Earth System Models (ESMs).

Unveiling the impact of soil methane sink on atmospheric methane concentrations in 2020.

In 2020, anthropogenic methane (CH4) emissions decreased due to COVID-19 containment policies, but there was a substantial increase in the concentration of atmospheric CH4. Previous research suggested that this abnormal increase was linked to higher wetland CH4 emissions and a decrease in the atmospheric CH4 sink. However, the impact of changes in the soil CH4 sink remained unknown. To address this, we utilized a process-based model to quantify alterations in the soil CH4 sink of terrestrial ecosystems between 2019 and 2020. By implementing the model with various datasets, we consistently observed an increase in the global soil CH4 sink, reaching up to 0.35 ± 0.06 Tg in 2020 compared to 2019. This increase was primarily attributed to warmer soil temperatures in northern high latitudes. Our results emphasize the importance of considering the CH4 sink in terrestrial ecosystems, as neglecting this component can lead to an underestimation of both emission increases and reductions in atmospheric CH4 sink capacity. Furthermore, these findings highlight the potential role of increased soil warmth in terrestrial ecosystems in slowing the growth of CH4 concentrations in the atmosphere.

Prof Diana Wall: A pioneering researcher and advocate of global soil biodiversity

Prof Diana Wall: A pioneering researcher and advocate of global soil biodiversity

Constraints and Drivers of Dissolved Fluxes of Pyrogenic Carbon in Soil and Freshwater Systems: A Global Review and Meta‐Analysis

AbstractPyrogenic carbon (PyC) is a significant component of the global soil carbon pool due to its longer environmental persistence than other soil organic matter components. Despite PyC's persistence in soil, recent work has indicated that it is susceptible to loss processes such as mineralization and leaching, with the significance and magnitude of these largely unknown at the hillslope and watershed scales. We present a review of the work concerning dissolved PyC transport in soil and freshwater. Our analysis found that the primary environmental controls on dissolved PyC (dPyC) transport are the formation conditions and quality of the PyC itself, with longer and higher temperature charring conditions leading to less transport of dPyC. While correlations between dPyC and dissolved organic carbon in rivers and other pools are frequently reported, the slope of these correlations was pool‐dependent (i.e., soil‐water, precipitation, lakes, streams, rivers), suggesting site‐specific environmental controls. However, the lack of consistency in analytical techniques and sample preparation remains a major challenge to quantifying environmental controls on dPyC fluxes. We propose that future research should focus on the following: (a) consistency in methodological approaches, (b) more quantitative measures of dPyC in pools and fluxes from soils to streams, (c) turnover times of dPyC in soils and aquatic systems, and (d) improved understanding of how mechanisms controlling the fate of dPyC in dynamic post‐fire landscapes interact. With more refined quantitative information about the controls on dPyC transport at the hillslope and landscape scale, we can increase the accuracy and utility of global carbon models.

Unveiling trends and environmental impacts of global black soil crop production: A comprehensive assessment

Black soil (BS) is essential for in global crop production, yet long-term cultivation of crops on BS has significantly impacted its sustainable use. Evaluation of crop production's environmental impacts on BS is imperative. Existing datasets often lack specificity and are insufficient to achieve the task. This study constructed a tailored inventory for crop production on BS to analyze its environmental impacts. The results reveal an increasing trend in major crop production on BS from 274.02 million tons (Mt) (2000) to 351.73 Mt (2010) and 442.80 Mt (2015). The top five producers — Russia, China, the United States, Ukraine, and Argentina—contribute about 80 % of BS crop production. The environmental impact of global BS crop production has been steadily increasing. Russia plays an influential role in the environmental impacts of crop production on BS, with land use, green water use, blue water use, and greenhouse gas emissions reaching 42.06 million hectares, 226.01 billion m3, 9.02 billion m3, and 166.74 Mt CO2-eq, respectively, in 2015. Productivity consistently promoted the use of green and blue water across these five countries. Environmental efficiency was the largest contributor to GHGs in the five countries rich in BS resources. The research also notes a shift in maize's environmental impact transfer pattern over time. China and the United States gradually relinquished their positions as the primary bearers of environmental impact, being replaced by Ukraine and Russia. Several suggestions are proposed to enhance BS's sustainable management, emphasizing the importance of legal frameworks and sustainable agricultural practices.

Evaluating the contribution of methanotrophy kinetics to uncertainty in the soil methane sink

The oxidation of atmospheric methane by soil microbes is an important natural sink for a potent greenhouse gas. However, estimates of the current and future soil methane sink are highly uncertain. Here we assessed the extent to which methanotrophy enzyme kinetics contribute to uncertainty in projections of the soil methane sink. We generated a comprehensive compilation of methanotrophy kinetic data from modern environments and assessed the patterns in kinetic parameters present in natural samples. Our compiled data enabled us to quantify the global soil methane sink through two idealized calculations comparing first-order and Michaelis–Menten models of kinetics. We show that these two kinetic models diverge only under high atmospheric CH4 scenarios, where first-order rate constants slightly overestimate the soil methane sink size, but produce similar predictions at modern atmospheric concentrations. Our compilation also shows that the kinetics of methanotrophy in natural soil samples is highly variable—both the V max (oxidation rate at saturation) and KM (half-saturation constant) in natural samples span over six orders of magnitude. However, accounting for the correlation we observe between V max and KM reduces the range of calculated uptake rates by as much as 96%. Additionally, our results indicate that variation in enzyme kinetics introduces a similar magnitude of variation in the calculated soil methane sink as temperature sensitivity. Systematic sampling of methanotroph kinetic parameters at multiple spatial scales should therefore be a key objective for closing the budget on the global soil methane sink.

Synthetic Microbial Community Promotes Bacterial Communities Leading to Soil Multifunctionality in Desertified Land.

Soil desertification is an important challenge in global soil management, and effectively and stably restoring soil function is an urgent problem. Using synthetic microbial communities (SynComs) is a burgeoning microbial strategy aimed at enhancing soil nutrients through functional synergies among diverse microorganisms; nevertheless, their effectiveness in restoring desertified soils remains unknown. In this study, we conducted a two-year field experiment using a SynCom constructed by in situ probiotic bacteria and set up control, chemical fertilizer, and combined SynCom-chemical fertilizer (combined fertilizer) treatments to investigate the linkage between microbial communities and soil multifunctionality in the soil surface layer (0-10 cm). Both the bacterial and fungal communities differed the most under the combined fertilizer treatment compared to the control. The bacterial communities differed more under treatments of the SynCom than the chemical fertilizer, while the fungal communities differed more under the chemical fertilizer treatment than the SynCom treatment. Regarding soil function, the SynCom strengthened the correlation between enzyme activities and both bacterial communities and functional properties. pH and available potassium were the main influencing factors under the chemical fertilizer and combined fertilizer treatments. The beta-diversity of the bacterial communities was significantly correlated with soil multifunctionality. Random forest analyses showed that the SynCom significantly enhanced the bacterial communities, driving soil multifunctionality, and that some potential microbial taxa drove multiple nutrient cycles simultaneously. In summary, the SynCom effectively increased the abundance of most carbon, nitrogen, and phosphorus functional genes as well as soil enzyme activities. The bacterial community composition contributed significantly to soil multifunctionality. Hence, the development of novel microbial agents holds significant potential for improving soil functionality and managing desertification.

Temporal and Spatial Soil Moisture–Precipitation Coupling Relationships Over the Tibetan Plateau

AbstractSoil moisture can significantly influence weather and climate via land‒atmosphere interactions over the Tibetan Plateau. However, the temporal and spatial preferences of precipitation for soil moisture anomalies and the underlying mechanisms over the plateau have not been determined. Using multiple satellite data sets (including Global Precipitation Measurement precipitation data and Soil Moisture Active Passive and Advanced SCATterometer soil moisture data) and ERA5 reanalysis data, the temporal and spatial soil moisture–precipitation coupling (SMPC) relationships in seven summers during 2015–2021 over the plateau are quantified based on a percentile‐based method. The satellite observations show prevalent positive temporal SMPC across the plateau, indicating that wetter‐than‐normal soil conditions tend to lead to more afternoon precipitation. While ERA5 generally aligns with satellite findings, it underestimates areas with positive temporal SMPC. Both the satellite and ERA5 data show that spatial SMPC relationships are usually statistically insignificant, but a few regions show significant positive relationships, that is, precipitation is more likely to occur over soils wetter than the surrounding soils. Moreover, the satellite observations suggest an inter‐event positive correlation between the temporal and spatial SMPC relationships. ERA5 agrees with the satellite‐based results over the western plateau but shows discrepancies over the eastern plateau. The temporal and spatial variations in soil moisture modulate the partitioning of surface heat fluxes, planetary boundary layer height, and lifting condensation level, promoting moist convection and afternoon precipitation. The findings from this study shed new light on SMPC and have important implications for precipitation forecasting over the plateau.

Global soil metagenomics reveals distribution and predominance of Deltaproteobacteria in nitrogen-fixing microbiome

BackgroundBiological nitrogen fixation is a fundamental process sustaining all life on earth. While distribution and diversity of N2-fixing soil microbes have been investigated by numerous PCR amplicon sequencing of nitrogenase genes, their comprehensive understanding has been hindered by lack of de facto standard protocols for amplicon surveys and possible PCR biases. Here, by fully leveraging the planetary collections of soil shotgun metagenomes along with recently expanded culture collections, we evaluated the global distribution and diversity of terrestrial diazotrophic microbiome.ResultsAfter the extensive analysis of 1,451 soil metagenomic samples, we revealed that the Anaeromyxobacteraceae and Geobacteraceae within Deltaproteobacteria are ubiquitous groups of diazotrophic microbiome in the soils with different geographic origins and land usage types, with particular predominance in anaerobic soils (paddy soils and sediments).ConclusionOur results indicate that Deltaproteobacteria is a core bacterial taxon in the potential soil nitrogen fixation population, especially in anaerobic environments, which encourages a careful consideration on deltaproteobacterial diazotrophs in understanding terrestrial nitrogen cycling.D657cgwaNSF-wLxbnJEePrVideo

Erratum for the Research Article "Size, distribution, and vulnerability of the global soil inorganic carbon" by Y. Huang et al.

Erratum for the Research Article "Size, distribution, and vulnerability of the global soil inorganic carbon" by Y. Huang et al.

Microbial ecology of the deep terrestrial subsurface.

The terrestrial subsurface hosts microbial communities that, collectively, are predicted to comprise as many microbial cells as global surface soils. Although initially thought to be associated with deposited organic matter, deep subsurface microbial communities are supported by chemolithoautotrophic primary production, with hydrogen serving as an important source of electrons. Despite recent progress, relatively little is known about the deep terrestrial subsurface compared to more commonly studied environments. Understanding the composition of deep terrestrial subsurface microbial communities and the factors that influence them is of importance because of human-associated activities including long-term storage of used nuclear fuel, carbon capture, and storage of hydrogen for use as an energy vector. In addition to identifying deep subsurface microorganisms, recent research focuses on identifying the roles of microorganisms in subsurface communities, as well as elucidating myriad interactions-syntrophic, episymbiotic, and viral-that occur among community members. In recent years, entirely new groups of microorganisms (i.e. candidate phyla radiation bacteria and Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoloarchaeota, Nanoarchaeota archaea) have been discovered in deep terrestrial subsurface environments, suggesting that much remains unknown about this biosphere. This review explores the historical context for deep terrestrial subsurface microbial ecology and highlights recent discoveries that shape current ecological understanding of this poorly explored microbial habitat. Additionally, we highlight the need for multifaceted experimental approaches to observe phenomena such as cryptic cycles, complex interactions, and episymbiosis, which may not be apparent when using single approaches in isolation, but are nonetheless critical to advancing our understanding of this deep biosphere.

Constrained Iterative Adaptive Algorithm for the Detection and Localization of RFI Sources Based on the SMAP L-Band Microwave Radiometer

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas.

Spatial downscaling of SMAP soil moisture estimation using multiscale geographically weighted regression during SMAPVEX16

Passive microwave remote sensing missions such as Soil Moisture Active Passive (SMAP) are widely used for global soil moisture (SM) estimation. But the low spatial resolution of passive microwave SM products limits their applications at regional and local scales (normally 1–10 km). Using high-resolution auxiliary data to downscale passive microwave SM products is the primary way to obtain high-resolution SM data. However, the existing SM downscaling methods do not entirely account for the scale differences in the impact of various variables on the distribution of SM. To solve this problem, this paper introduced MGWR (Multiscale Geographically Weighted Regression) as a novel way to analyze the scale differences of normalized difference vegetation index (NDVI) and land surface temperature (LST) on the spatial pattern of SM. Accordingly, a new spatial downscaling algorithm for SMAP SM products is proposed and compared with the geographically weighted regression (GWR) algorithm and moving window algorithm (MW). The algorithm was applied to data taken during the SMAP Validation Experiment 2016 (SMAPVEX16), and the downscaled SM evaluated with in situ SM and aircraft observations. These results show that: 1) the SM downscaling conversion function, based on the MGWR, effectively reveals how different surface parameters relate to SM, with LST influencing SM globally and NDVI affecting it locally, 2) compared with GWR and MW, the 1 km SM obtained by the MGWR-based downscaling method showed better spatial agreement with airborne SM in the semiarid region with the ubRMSE decreasing by 0.023 m3/m3 and 0.026 m3/m3 for GWR and MW respectively, 3) when evaluating the 1 km SM with in situ SM in the semiarid region, the SM obtained through MGWR exhibited lower ubRMSE values as compared to both GWR and MW. Specifically, the median ubRMSE values for MGWR, GWR, and MW were 0.043 m3/m3, 0.053 m3/m3, and 0.094 m3/m3, respectively. In conclusion, this study demonstrates that MGWR can effectively improve the accuracy of downscaled SM in semiarid regions.

Integrating Omics Approaches for Climate-Resilient Crops: A Comprehensive Review

Climate change is a looming threat to global agriculture, impacting temperature, rainfall patterns, pest dynamics, and soil quality. These challenges transcend numerous crops vital for global food security. Drought, soil acidity, and nutrient fluctuations induced by climate change impede crop productivity, necessitating the development of breeding strategies to produce climate-resilient varieties. This review delves into the integration of omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, to bolster breeding programs across diverse crops. By harnessing high-throughput technologies, researchers gain insights into the genetic and molecular mechanisms underlying traits such as stress resistance, yield, and disease tolerance. The cultivation of elite cultivars with enhanced stress tolerance is paramount for sustainable agriculture in the face of climate change. Various breeding approaches, encompassing functional genomics and mutagenomics, are explored alongside the application of genome editing tools like CRISPR/Cas9 and TALEN for targeted trait enhancement. Through the integration of multi-omics data, novel genetic targets are unearthed, facilitating the development of crop varieties resilient to climate-induced stressors beyond maize. This review underscores the significance of multi-omics in crop breeding and highlights strides made toward climate-resilient crop production. Understanding crop responses to abiotic stresses induced by climate change is imperative for the development of resilient varieties. The integration of modern genetics into classical breeding methods aims to cultivate stress-resistant cultivars, mitigating food security risks. Multi-omics approaches play pivotal roles in unraveling crop performance and stress tolerance mechanisms under diverse environmental conditions. Moving forward, the integration of multi-omics approaches will pinpoint candidate genes and pathways, enabling precision breeding to enhance crop performance amidst changing climates. These endeavors will propel the advancement of climate-resilient crops, safeguarding global food security in the face of climate change's challenges.

Modeling microbial carbon fluxes and stocks in global soils from 1901 to 2016

Abstract. Soil microbes play a crucial role in the carbon (C) cycle; however, they have been overlooked in predicting the terrestrial C cycle. We applied a microbial-explicit Earth system model – the Community Land Model-Microbe (CLM-Microbe) – to investigate the dynamics of soil microbes during 1901 to 2016. The CLM-Microbe model was able to reproduce the variations of gross (GPP) and net (NPP) primary productivity, heterotrophic (HR) and soil (SR) respiration, microbial (MBC) biomass C in fungi (FBC) and bacteria (BBC) in the top 30 cm and 1 m, and dissolved (DOC) and soil organic C (SOC) in the top 30 cm and 1 m during 1901–2016. During the study period, simulated C variables increased by approximately 12 PgC yr−1 for HR, 25 PgC yr−1 for SR, 1.0 PgC for FBC and 0.4 PgC for BBC in 0–30 cm, and 1.2 PgC for FBC and 0.7 PgC for BBC in 0–1 m. Increases in microbial C fluxes and pools were widely found, particularly at high latitudes and in equatorial regions, but we also observed their decreases in some grids. Overall, the area-weighted averages of HR, SR, FBC, and BBC in the top 1 m were significantly correlated with those of soil moisture and soil temperature in the top 1 m. These results suggested that microbial C fluxes and pools were jointly governed by vegetation C input and soil temperature and moisture. Our simulations revealed the spatial and temporal patterns of microbial C fluxes and pools in response to environmental change, laying the foundation for an improved understanding of soil microbial roles in the global terrestrial C cycle.

Visible and infrared lab spectroscopy for soil texture classification: Analysis of entire spectra v/s reduced spectra

Soil, directly or indirectly, impacts seven of the Sustainable Development Goals set by the United Nations. Soil texture is a fundamental physical property that controls other properties ranging from soil stability, erodibility, compaction to water holding capacity, nutrient availability and fertility. While Visible Near-Infrared and Shortwave-Infrared (VNIR/SWIR) laboratory spectroscopy has been already used for soil texture classification, Mid Infrared (MIR) region and unison of the VNIR/SWIR and MIR region warrant quantification. Thus, the objectives of this work were: (1) to investigate the critical spectral regions for soil texture classification, (2) to compare the use of entire spectral bands v/s subset of spectral bands obtained via the feature selection method, and (3) to quantify the degree of misclassification in the neighbouring classes for a given textural class. This study utilized an ICRAF–ISRIC global soil spectral library consisting of 3643 VNIR/SWIR, MIR soil spectra and the USDA soil textural classes. Two classifiers were used for soil texture classification using the three databases (VNIR/SWIR, MIR, VNIR + MIR) with entire spectral bands and three databases with only the Partial Information Correlation (PIC) selected bands (VNIR/SWIR_PIC, MIR_PIC, VNIR + MIR_PIC). The mean confusion matrix, overall accuracy and kappa were calculated to evaluate classifiers' performances over 100 iterations. Two additional measures were proposed for partitioning inaccuracies between neighbouring and far classes: Correct-Neighbouring-Far classes distribution matrix and added neighbourhood accuracy. This work highlighted that: (1) the combined VNIR/SWIR + MIR region provided the best texture classification, followed by MIR and then VNIR/SWIR region, (2) the use of PIC selected bands provided lower classification performances when compared to using all bands but a massive reduction in the number of bands allowing to reduce model complexity, (3) the misclassifications were predominantly in the neighbouring classes rather than far classes, for a given texture class. Finally, the textural classes with poor classification performance had low areal and sample representation. These insights may launch a number of soil texture classification studies worldwide based on global soil spectral databases, further helping map soils and the associated ecosystem services it provides.

Global Atlas of Methane Metabolism Marker Genes in Soil.

Methane, a greenhouse gas, plays a pivotal role in the global carbon cycle, influencing the Earth's climate. Only a limited number of microorganisms control the flux of biologically produced methane in nature, including methane-oxidizing bacteria, anaerobic methanotrophic archaea, and methanogenic archaea. Although previous studies have revealed the spatial and temporal distribution characteristics of methane-metabolizing microorganisms in local regions by using the marker genes pmoA or mcrA, their biogeographical patterns and environmental drivers remain largely unknown at a global scale. Here, we used 3419 metagenomes generated from georeferenced soil samples to examine the global patterns of methane metabolism marker gene abundances in soil, which generally represent the global distribution of methane-metabolizing microorganisms. The resulting maps revealed notable latitudinal trends in the abundances of methane-metabolizing microorganisms across global soils, with higher abundances in the sub-Arctic, sub-Antarctic, and tropical rainforest regions than in temperate regions. The variations in global abundances of methane-metabolizing microorganisms were primarily governed by vegetation cover. Our high-resolution global maps of methane-metabolizing microorganisms will provide valuable information for the prediction of biogenic methane emissions under current and future climate scenarios.

Studying long term relationship between carbon Emissions, Soil, and climate Change: Insights from a global Earth modeling Framework

The persistent increase in greenhouse gas (GHG) emissions, notably carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), since the mid-20th century has been a key driver of significant climate alterations. This study investigates the complex feedback mechanisms that both influence and are influenced by global climate dynamics, soil processes, and GHG emissions. Our statistical approach incorporates correlation measures, highlighting the limitations of such analyses, namely their inability to confirm causality, sensitivity to outliers, and the exclusive capture of linear relationships. Geographically Weighted Regression (GWR) models reveal spatial variations in the relationship between environmental factors and GHGs, while Path Analysis aids in delineating direct and indirect influences among variables. The research pinpoints significant spatial heterogeneity in the impacts of economic and environmental factors on GHGs, underscoring the necessity of localized strategies for climate change mitigation and sustainable land management. This study also identifies potential threats to agricultural productivity due to soil degradation, which hinder climate adaptation efforts. Our findings advocate for a concerted global response to reduce GHG emissions and address the challenges posed by the interplay of climate change, soil dynamics, and GHG emissions.

Genomic fingerprints of the world's soil ecosystems.

Despite the explosion of soil metagenomic data, we lack a synthesized understanding of patterns in the distribution and functions of soil microorganisms. These patterns are critical to predictions of soil microbiome responses to climate change and resulting feedbacks that regulate greenhouse gas release from soils. To address this gap, we assay 1,512 manually curated soil metagenomes using complementary annotation databases, read-based taxonomy, and machine learning to extract multidimensional genomic fingerprints of global soil microbiomes. Our objective is to uncover novel biogeographical patterns of soil microbiomes across environmental factors and ecological biomes with high molecular resolution. We reveal shifts in the potential for (i) microbial nutrient acquisition across pH gradients; (ii) stress-, transport-, and redox-based processes across changes in soil bulk density; and (iii) greenhouse gas emissions across biomes. We also use an unsupervised approach to reveal a collection of soils with distinct genomic signatures, characterized by coordinated changes in soil organic carbon, nitrogen, and cation exchange capacity and in bulk density and clay content that may ultimately reflect soil environments with high microbial activity. Genomic fingerprints for these soils highlight the importance of resource scavenging, plant-microbe interactions, fungi, and heterotrophic metabolisms. Across all analyses, we observed phylogenetic coherence in soil microbiomes-more closely related microorganisms tended to move congruently in response to soil factors. Collectively, the genomic fingerprints uncovered here present a basis for global patterns in the microbial mechanisms underlying soil biogeochemistry and help beget tractable microbial reaction networks for incorporation into process-based models of soil carbon and nutrient cycling.IMPORTANCEWe address a critical gap in our understanding of soil microorganisms and their functions, which have a profound impact on our environment. We analyzed 1,512 global soils with advanced analytics to create detailed genetic profiles (fingerprints) of soil microbiomes. Our work reveals novel patterns in how microorganisms are distributed across different soil environments. For instance, we discovered shifts in microbial potential to acquire nutrients in relation to soil acidity, as well as changes in stress responses and potential greenhouse gas emissions linked to soil structure. We also identified soils with putative high activity that had unique genomic characteristics surrounding resource acquisition, plant-microbe interactions, and fungal activity. Finally, we observed that closely related microorganisms tend to respond in similar ways to changes in their surroundings. Our work is a significant step toward comprehending the intricate world of soil microorganisms and its role in the global climate.

Organic matter decomposition in mountain peatlands: effects of substrate quality and peatland degradation

Abstract Background and Aims Peatlands occupy only 3% of Earth’s terrestrial lands but store about one-third of global soil carbon. However, these large carbon stocks are currently under threat due to peatland degradation, where altered hydrological balance could enhance peat oxidation; thus releasing large amounts of CO2 into the atmosphere. We investigated the interactive effects of substrate quality, peat depth and peatland degradation on the decomposition rate of organic matter in peatlands by way of a field incubation experiment. Methods We incubated high-quality fresh peat and a lower-quality degraded peat substrate at three different depths (5, 15, and 30 cm) in two (intact and degraded) mountain peatlands for 18 months. Our results indicated that there is a significant interactive effect of substrate quality, peat depth, and peatland type on the peat decomposition rate. Results The fresh peat showed significantly higher decomposition rates than the degraded peat substrate, likely due to the high percentage of bioavailable carbon in the fresh moss substrate. In the degraded peatland, fresh peat at 30 cm showed no mass loss during the incubation period, likely due to the high-water table creating anaerobic conditions. The fresh peat incubated in the intact peatland showed a higher decomposition rate than the same substrate incubated in the degraded peatland due to the comparatively lower water table in the intact peatland. Conclusions Our findings indicate that the quality of the substrate being decomposed and the depth of the water table act as the main factors affecting the decomposition rate in mountain peatlands.