DOI: 10.5061/dryad.jh9w0vtk1
title: Soil microbial composition and physicochemical data for Southwestern U.S. dryland sites

description: Escalating soil degradation and ecosystem service losses across global
 drylands highlight a clear need for strategic approaches for active
 restoration. There is growing interest in using soil microbe inoculation
 or “soil transplants'' from intact reference sites to promote
 dryland restoration success. Yet, use of soil inoculation treatments is
 often not guided by a preliminary understanding of soil community
 structure and what organisms and/or functions are lacking in degraded
 communities. This study explores how the composition and diversity of
 microbial communities differ among degraded, revegetated, and intact
 dryland sites. We collected soil samples from degraded, revegetated, and
 intact plots at seven different dryland sites across the southwestern
 United States, sequenced the 16S and ITS rRNA gene regions from extracted
 DNA for bacteria/archaeal and fungal communities respectively, and
 analyzed differences in microbial community composition among plots. We
 found relatively small, but significant differences in soil microbial
 communities between degraded and intact sites. However, we found no
 differences in microbial communities between degraded and revegetated
 sites, suggesting degraded sites may require active interventions beyond
 revegetation, such as direct microbial inoculation, to replenish soil
 microbial communities. Several indicator species of intact sites were
 identified, including species belonging to groups of biocrust-associated
 bacteria and dark septate endophytic fungi, which may be good targets for
 inoculation treatments. These findings advance our understanding of how
 degradation and revegetation may affect microbial communities and suggest
 several microbial taxa that may be useful for inoculation, which could
 have actionable implications, and thus improve outcomes, in dryland
 restoration.
description: These datasets were created by collecting three replicate 10 gram
 soil samples from degraded, revegetated, and intact/reference plots from
 seven different dryland sites across the southwestern U.S. These sites are
 part of a networked restoration research project called RestoreNet. Learn
 more here: https://www.usgs.gov/centers/southwest-biological-science-center/science/restorenet-distributed-field-trial-network  DNA was extracted from all samples and the 16S (bacteria and archaea) and ITS (fungi) gene regions were amplified and sequenced. Data were filtered using the dada2 package in R Studio and the Silva and UNITE databases were used to assign taxonomy. Lastly, data with unknown taxonomy at the phylum level, DNA blanks, non-template controls, and chloroplasts and mitochondria were removed to generate these datasets. Microbial datasets include the abundances of bacteria, archaea, and fungi taxa found in dryland soil samples at degraded, revegetation, and intact/reference sites. Physicochemical soil characteristics were also measured through laboratory analysis for all soil samples. These values are reported in the physical soil dataset.
description: # RestoreNet Microbiome and Soil Data
 [https://doi.org/10.5061/dryad.jh9w0vtk1](https://doi.org/10.5061/dryad.jh9w0vtk1) This data collection includes two soil microbial datasets (bacteria/archaea and fungi) and one physical soil and site characteristic dataset. The soil microbial dataset includes the abundances of bacteria and archaea found in dryland soil samples at degraded, revegetated, and intact/reference sites. The physical soil and site characteristic dataset includes some basic characteristics of each of these sites, various physicochemical characteristics of each soil sample, and information on the revegetation implemented at certain sites. ## Description of the data and file structure **Bacteria/archaea (16S) Data:** * Column A is the unique ID of each ASV (Amplicon Sequence Variant, the taxonomic unit used in these data). ASV IDs are assigned in order of abundance, with 'ASV_1' being the most abundant species. * Columns B through BF are the soil sample IDs * The first part of the label (ie "BART") is the name of the site at which the sample was taken * The second part of the label (ie "DA") is the site condition and the replicate * "D" = degraded, "I" = intact/reference, and "R" = revegetated * "A", "B", and "C" denote replicates * So, "BART_DA" is replicate sample A from the degraded plot at site BART * Columns BG through BL are the taxonomic assignments of each ASV at the kingdom through genus level * The value next to each ASV in each column is the abundance of each bacteria or archaea found in the respective soil sample * So, for example, cell B5 shows that there were 454 specimens of a species of *Microverga* bacteria found in this sample **Fungi (ITS) Data:** * Column A is the unique ID of each ASV (Amplicon Sequence Variant, the taxonomic unit used in these data). ASV IDs are assigned in order of abundance, with 'ASV_1' being the most abundant species. * Columns B through BF are the soil sample IDs * The first part of the label (ie "BART") is the name of the site at which the sample was taken * The second part of the label (ie "DA") is the site condition and the replicate * "D" = degraded, "I" = intact/reference, and "R" = revegetated * "A", "B", and "C" denote replicates * So, "BART_DA" is replicate sample A from the degraded plot at site BART * Columns BG through BM are the taxonomic assignments of each ASV at the kingdom through species level * The value next to each ASV in each column is the abundance of each fungal taxa found in the respective soil sample * So, for example, cell B4 shows that there were 1153 specimens of *Gibberella intricans* found in this sample **Soil and Site Data:** * Column A is the soil sample ID * The first part of the label (ie "BART") is the name of the site at which the sample was taken * The second part of the label (ie "DA") is the site condition and the replicate * "D" = degraded, "I" = intact/reference, and "R" = revegetated * "A", "B", and "C" denote replicates * So, "BART_DA" is replicate sample A from the degraded plot at site BART * Columns B through F include geographic and aridity information of each site * Columns G through V are the physicochemical characteristics of each sample * Bulk density - grams per cubic centimeter (g/ccm) * Soil porosity - percentage (%) * Water content - grams water per gram of soil (g/g) * pH - unitless * Electrical conductivity (EC) - millimhos per centimeter (mmhos/cm) * Lime content - low, medium, high, or none * Organic matter (OM) - percentage (%) * Nitrate (NO3-N) - parts per million (ppm) * Phosphorus (P) - parts per million (ppm) * Potassium (K) - parts per million (ppm) * Zinc (Zn) - parts per million (ppm) * Iron (Fe) - parts per million (ppm) * Manganese (Mn) - parts per million (ppm) * Copper (Cu) - parts per million (ppm) * Boron (B) - parts per million (ppm) * Columns W through Z include information about the revegetation that was implemented at certain sites, including: * the category of species mix used (warm/cool season) * the USDA plant codes of the species used for revegetation in the plot sampled * the number of surviving transplanted seedlings after 3 years * the survival rate of transplanted seedlings after 3 years * cells with 'n/a' indicate that revegetation was not implemented at these sites and therefore this information is not applicable ## Code/Software Microbial datasets were generated using R Studio. The "idemp" function was used to demultiplex reads from the dataset generated from the Illumina MiSeq sequencing platform, and the "cutadapt" function was used to trim sequences. The *dada2* package was used to denoise and filter the data in order to produce this dataset. * idemp: [https://github.com/yhwu/idemp](https://github.com/yhwu/idemp) * cutadapt: [https://cutadapt.readthedocs.io/en/stable/](https://cutadapt.readthedocs.io/en/stable/) * dada2: [https://benjjneb.github.io/dada2/tutorial.html](https://benjjneb.github.io/dada2/tutorial.html)