Deciphering microbiomes dozens of meters under our feet and their edaphoclimatic and spatial drivers
Data files
Nov 08, 2023 version files 84.20 KB
Abstract
Microbes inhabiting deep soil layers are known to be different from their counterpart in topsoil, yet remain under investigation in terms of their structure, function, and how their diversity is shaped. The microbiome of deep soils (> 1 m) is expected to be relatively stable and highly independent from climatic conditions. Much less is known, however, on how these microbial communities vary along climate gradients. Here, we used amplicon sequencing to investigate bacteria, archaea, and fungi along fifteen 18-m depth profiles at 20–50 cm intervals across contrasting aridity conditions in semi-arid forest ecosystems of China's Loess Plateau. Our results showed that bacterial and fungal α diversity and bacterial and archaeal community similarity declined dramatically in topsoil and remained relatively stable in deep soil. Nevertheless, deep soil microbiome still showed the functional potential of N cycling, plant-derived organic matter degradation, resource exchange, and water coordination. The deep soil microbiome had closer taxa-taxa and bacteria-fungi associations and more influence of dispersal limitation than topsoil microbiome. Geographic distance was more influential in deep soil bacteria and archaea than in topsoil. We further showed that aridity was negatively correlated with deep-soil archaeal and fungal richness, archaeal community similarity, relative abundance of plant saprotroph, and bacteria-fungi associations, but increased the relative abundance of aerobic ammonia oxidation, manganese oxidation, and arbuscular mycorrhizal in the deep soils. Root depth, complexity, soil volumetric moisture, and clay play bridging roles in the indirect effects of aridity on microbes in deep soils. Our work indicates that even microbial communities and nutrient cycling in deep soil are susceptible to changes in water availability, with consequences for understanding the sustainability of dryland ecosystems and the whole-soil in response to aridification. Moreover, we propose that neglecting soil depth may underestimate the role of soil moisture in dryland ecosystems under future climate scenarios.
README
The files in Dryad contain the data necessary to reproduce the statistical analyses published in the manuscript "Deciphering microbiomes dozens of meters under our feet and their edaphoclimatic and spatial drivers".
The four datasets in the zipped file:
- Metadata.xlsx: Soil properties, environmental variables and sample information
- Microbial properties.xlsx: microbial composition and diversity
- Network properties.xlsx: Soil microbial co-occurrence network properties
- MST_assembly.xlsx: The modified stochasticity ratio (MST) was used to infer microbial community assembly processes
Variable description
- MAP: Mean annual precipitation
- Aridity: 1-precipitation/evapotranspiration
- Soil volumetric moisture: SVWC, ratio of volume occupied by water in soil to total soil volume
- Specific surface area: SSA, total surface area per unit mass of soil sample
- Median size: MS, particle size corresponding to 50% of the cumulative percentage of the size distribution
- pH: Soil pH
- SOC: Soil organic carbon
- MBC: Microbial biomass carbon
- TN: Soil total nitrogen
- NH4+-N: Ammonium nitrogen
- NO3--N: Nitrate nitrogen
- TP: Soil total phosphorus
- AP: Available phosphorus
- Root biomass: root dry weight per unit soil area
- meanFunction: Soil multi-nutrient cycling index
- Station: Abbreviation of sampling site
- Afforestation: Ecosystem type of aboveground vegetation
- Observed richness: Alpha diversity
- PCoA1: The first principal component of PCoA
- bac_MST: Modified stochasticity ratio of bacteria
- arc_MST: Modified stochasticity ratio of archaea
- fun_MST: Modified stochasticity ratio of fungi
If you have any other questions about this Dataset, you can directly contact the corresponding authour