DOI: 10.5061/dryad.mw6m905th
title: Data from: Spatial patterning of soil microbial communities created by fungus-farming termites

description: Spatially overdispersed mounds of fungus-farming termites
 (Macrotermitinae) are hotspots of nutrient availability and primary
 productivity in tropical savannas, creating spatial heterogeneity in
 communities and ecosystem functions. These termites influence the local
 availability of nutrients in part by redistributing nutrients across the
 landscape, but the links between termite ecosystem engineering and the
 soil microbes that are the metabolic agents of nutrient cycling are little
 understood. We used DNA metabarcoding of soils from Odontotermes
 montanus mounds to examine the influence of termites on soil
 microbial communities in a semi-arid Kenyan savanna. We found that
 bacterial and fungal communities were compositionally distinct in
 termite-mound topsoils relative to the surrounding savanna, and that
 bacterial communities were more diverse on mounds. The higher microbial
 alpha and beta diversity associated with mounds created striking spatial
 patterning in microbial community composition, and boosted landscape-scale
 microbial richness and diversity. Selected enzyme assays revealed
 consistent differences in potential enzymatic activity, suggesting links
 between termite-induced heterogeneity in microbial community composition
 and the spatial distribution of ecosystem functions. We conducted a
 large-scale field experiment in which we attempted to simulate termites’
 effects on microbes by fertilizing mound-sized patches; this altered both
 bacterial and fungal communities, but in a different way than natural
 mounds. Elevated levels of inorganic nitrogen, phosphorus, and potassium
 may help to explain the distinctive fungal communities in termite-mound
 soils, but cannot account for the distinctive bacterial
 communities associated with mounds.
description: This dataset comprises the data and code used in our
 paper <em>Spatial patterning of soil microbial communities created
 by fungus-farming termites</em>. These data are mainly 16S and
 ITS metabarcode data from Kenyan soil samples associated with
 <em>Odontotermes</em> termite mounds, or taken from a large
 scale field experiment involving the addition of inorganic NPK fertilizer.
 The data also include measurements of extracellular enzyme activity taken
 from a subset of the soil samples. Methods are described in our paper, in
 the Materials and Methods section, and in Document S1.
description: Files are organized in two zip files
 (<strong>cluster.zip</strong> and
 <strong>local.zip</strong>) in order to preserve directory
 structure. An accompanying readme file (soil_microbes_readme.md) provides
 an overview of the dataset.
 <strong>cluster.zip</strong> includes our Illumina
 sequence data files and our pipeline for processing these files into OTU
 tables ready for analysis. The file
 <strong>soil_microbes.sh</strong> contains details of the
 pipeline. The code relies primarily on the OBITools pipeline (Boyer
 <em>et al</em>. 2016) and is intended to be executed on a
 Linux cluster. Data, metadata and scripts are provided in their own
 subfolders. Additional readme files accompany the data and metadata and
 are placed in the respective subfolders.
 <strong>local.zip</strong> includes code for
 analyzing the processed OTU tables generated by the pipeline in
 cluster.zip, as well as the separate dataset on extracellular enzyme
 activity. Data and metadata are provided in their own subfolders with
 accompanying readme files. Code is written in R and is separated into a
 number of files in the scripts subfolder. Scripts produce outputs in the
 figures, tables, and out folders. Previous outputs are included here. Some
 of these outputs, particularly RData files in the out folder, are used as
 inputs by other scripts. The shell
 script <strong>soil_microbes_R.sh</strong> provides a listing
 of the R scripts, and could in principle be used to run the analysis,
 though we anticipate that most users will be more interested in inspecting
 or executing smaller parts of the R code interactively.
 <strong>References</strong><br> Boyer F,
 Mercier C, Bonin A, <em>et al</em>. (2016) OBITOOLS: a
 UNIX-inspired software package for DNA metabarcoding.
 <strong>Molecular Ecology Resources</strong> 16,
 176-182<br>