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.

This dataset comprises the data and code used in our paper Spatial patterning of soil microbial communities created by fungus-farming termites. These data are mainly 16S and ITS metabarcode data from Kenyan soil samples associated with Odontotermes 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.

Files are organized in two zip files (cluster.zip and local.zip) in order to preserve directory structure. An accompanying readme file (soil_microbes_readme.md) provides an overview of the dataset.

cluster.zip includes our Illumina sequence data files and our pipeline for processing these files into OTU tables ready for analysis. The file soil_microbes.sh contains details of the pipeline. The code relies primarily on the OBITools pipeline (Boyer et al. 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.

local.zip 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 soil_microbes_R.sh 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.

References
Boyer F, Mercier C, Bonin A, et al. (2016) OBITOOLS: a UNIX-inspired software package for DNA metabarcoding. Molecular Ecology Resources 16, 176-182