DOI: 10.5061/dryad.h70rxwdqp
title: Deciphering microbiomes dozens of meters under our feet and their edaphoclimatic and spatial drivers

description: 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 (&gt; 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.
description: 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: 1. Metadata.xlsx:
 Soil properties, environmental variables and sample information 2.
 Microbial properties.xlsx: microbial composition and diversity 3. Network
 properties.xlsx: Soil microbial co-occurrence network properties 4.
 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