Mapping soil microbial residence time at the global scale

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.

Mapping turnover of dissolved organic carbon in global topsoil

Dissolved organic carbon (DOC), the labile fraction of organic carbon, is a predominant substrate for microbes. Therefore, the turnover of DOC dominates microbial respiration in soils. We compiled a global dataset (1096 data points) of the turnover rates of DOC in 0-30 cm soil profiles and integrated the data with a machine learning algorithm to develop a global map of DOC turnover rate in global topsoil. The global DOC turnover rate in 0-30 cm soil was averaged as 0.0087 day-1, with a considerable variation among biomes. The fastest DOC turnover rate was found in tropical forests (0.0175 day-1) and the lowest in tundra (0.0036 day-1), exhibiting a declining trend from low to high latitudes. The DOC turnover rate is primarily controlled by edaphic and climate factors, as confirmed by the analyses with the structural equation model and the Mental's test. With a machine learning algorithm, we produced global maps of DOC turnover rate at a monthly scale, which were further combined with a global dataset of DOC density to produce monthly maps of carbon mineralization from DOC turnover in topsoil. The annual carbon release from DOC was estimated as 27.98 Pg C year-1 from topsoil across the globe, with the largest contribution from forest biomes, followed by pasture and grassland. Tundra released the least carbon from DOC due to its low turnover rate suppressed by low temperatures. The biome- and global-scale information of DOC turnover rate and carbon release from DOC provide a benchmark for ecosystem models to better project soil carbon dynamics and their contributions to global carbon cycling in the changing environment.

Latitudinal shifts of soil microbial biomass seasonality

Soil microbes ultimately drive the mineralization of soil organic carbon and thus ecosystem functions. We compiled a dataset of the seasonality of microbial biomass carbon (MBC) and developed a semi-mechanistic model to map monthly MBC across the globe. MBC exhibits an equatorially symmetric seasonality between the Northern and Southern Hemispheres. In the Northern Hemisphere, MBC peaks in autumn and is minimal in spring at low latitudes (<25°N), peaks in the spring and is minimal in autumn at mid-latitudes (25°N to 50°N), while peaks in autumn and is minimal in spring at high latitudes (>50°N). This latitudinal shift of MBC seasonality is attributed to an interaction of soil temperature, soil moisture, and substrate availability. The MBC seasonality is inconsistent with patterns of heterotrophic respiration, indicating that MBC as a proxy for microbial activity is inappropriate at this resolution. This study highlights the need to explicitly represent microbial physiology in microbial models. The interactive controls of environments and substrate on microbial seasonality provide insights for better representing microbial mechanisms in simulating ecosystem functions at the seasonal scale.

Microbial community structure and functional genes drive soil priming effect following afforestation

Afforestation substantially modifies native soil organic carbon (SOC) decomposition via plant carbon inputs (the priming effect), and in turn, triggers vital biogeochemical processes that influence the regulation of soil carbon dynamics. Soil microbes are crucial in regulating the direction and magnitude of the priming effect. In the present study, we performed metagenomic sequencing and ¹³C-glucose labeling analyses of microbial communities and priming effects across a Robinia pseudoacacia afforestation chronosequence (14-, 20-, 30-, and 45-year-old stands) in the Loess Plateau in China, with adjacent farmland being selected as a control. Our results revealed that the cumulative priming effect across five sites along the afforestation chronosequence initially increased and approached a peak value in the 20-year-old stand, after which it declined. The priming effect was predominantly driven by the microbial community structure (i.e., the fungal-to-bacterial ratios and relative abundances of Proteobacteria and Actinobacteria), and stable C decomposition genes and C-degrading enzymes. Specifically, among the key functional genes correlated with priming effect, which were identified in orders Rhizobiales and Pseudonocardiales, considerably promoted SOC priming. Overall, our findings indicate that afforestation alters soil microbial community structure and function, particularly with respect to enhancing stable soil C decomposition genes, which may promote SOC priming. The findings of the present study could enhance our understanding of fresh C input-induced changes associated with C mineralization in the context of the revegetation of ecologically fragile areas.

Contribution of soil algae to the global carbon cycle

Soil photoautotrophic prokaryotes and micro-eukaryotes - known as soil algae - are, together with heterotrophic microorganisms, a constitutive part of the microbiome in surface soils. Similar to plants, they fix atmospheric carbon (C) through photosynthesis for their own growth, yet their contribution to global and regional biogeochemical C cycling still remains quantitatively elusive. Here, we compiled an extensive dataset on soil algae to generate a better understanding of their distribution across biomes and predict their productivity at a global scale by means of machine learning modelling. We found that, on average, (5.5 ± 3.4) × 10⁶ algae inhabit each gram of surface soil. Soil algal abundance especially peaked in acidic, moist and vegetated soils. We estimate that, globally, soil algae take up around 3.6 Pg C per year, which corresponds to c. 6% of the net primary production of terrestrial vegetation. We demonstrate that the C fixed by soil algae is crucial to the global C cycle and should be integrated into land-based efforts to mitigate C emissions.