Data from: Direct and diffuse cross-kingdom interactions in plant microbiome assembly
Data files
Nov 06, 2026 version files 16.60 MB
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BAC_ASV.csv
10.82 MB
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BAC_SAM.csv
91.14 KB
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BAC_TAX.csv
1.21 MB
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BacFunPhyloseqObjects.RData
829.70 KB
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FUN_ASV.csv
647.32 KB
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FUN_SAM.csv
67.96 KB
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FUN_TAX.csv
84.65 KB
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Hammer_Networks_Diversity.pdf
253.02 KB
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Hammer_Networks_Diversity.Rmd
5.36 KB
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Hammer_Networks_Phylogenetics.pdf
269.20 KB
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Hammer_Networks_Phylogenetics.Rmd
13.13 KB
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Hammer_Networks_PrepData.pdf
244.40 KB
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Hammer_Networks_PrepData.Rmd
2.70 KB
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Hammer_Networks_SpiecEasi.pdf
277.09 KB
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Hammer_Networks_SpiecEasi.Rmd
26.09 KB
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Hammer_Networks_titanEnvBiotic.pdf
267.45 KB
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Hammer_Networks_titanEnvBiotic.Rmd
17.94 KB
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Hammer_Networks_variancePartitioning.pdf
320.42 KB
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Hammer_Networks_variancePartitioning.Rmd
99.52 KB
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InteractionData.xlsx
40.70 KB
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Interactors.xlsx
21.09 KB
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Leaf_bacteria_phylotree.nwk
209.57 KB
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Leaf_fungi_phylotree.nwk
154.95 KB
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NodesList.xlsx
28.32 KB
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README.md
20.67 KB
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Root_bacteria_phylotree.nwk
407.55 KB
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Root_fungi_phylotree.nwk
166.99 KB
Abstract
We used ITS and 16S rRNA gene amplicon sequencing to characterize bacterial and fungal communities of switchgrass (Panicum virgatum L.) leaves and roots at 14 sites spanning mountain to coastal ecoregions of North Carolina, USA. We used ecological network inference to identify putative direct and diffuse interactions and variance partitioning to assess the contributions of spatial, environmental, and biotic interactions to microbial community composition. Counts of ASVs, microbial community composition, phylogenetics, network analysis, and variance partitioning data are included.
Supplementary Data and Analysis
Data and analyses associated with this project.
Experiment description
16S and ITS rRNA sequence analysis was used to characterize bacterial and fungal communities of Panicum virgatum leaves and roots collected from 14 sites in North Carolina, USA. We analyzed putative direct and diffuse interactions using ecological network inference and partitioned variance explained in microbial community composition by spatial, environmental, and biotic interactions.
File list
Data
- BAC_ASV.csv
- BAC_SAM.csv
- BAC_TAX.csv
- FUN_ASV.csv
- FUN_SAM.csv
- FUN_TAX.csv
- BacFunPhyloseqObjects.RData
- Leaf_bacteria_phylotree.nwk
- Leaf_fungi_phylotree.nwk
- Root_bacteria_phylotree.nwk
- Root_fungi_phylotree.nwk
- InteractionData.xlsx
- NodesList.xlsx
- Interactors.xlsx
Scripts and analyses
- Hammer_Networks_PrepData.Rmd
- Hammer_Networks_PrepData.pdf
- Hammer_Networks_Phylogenetics.Rmd
- Hammer_Networks_Phylogenetics.pdf
- Hammer_Networks_Diversity.Rmd
- Hammer_Networks_Diversity.pdf
- Hammer_Networks_SpiecEasi.Rmd
- Hammer_Networks_SpiecEasi.pdf
- Hammer_Networks_variancePartitioning.Rmd
- Hammer_Networks_variancePartitioning.pdf
- Hammer_Networks_titanEnvBiotic.Rmd
- Hammer_Networks_titanEnvBiotic.pdf
File descriptions
Data
BAC_ASV.csv + FUN_ASV.csv – ASV tables for bacterial and fungal communities from bioinformatics processing of sequence data. Columns include "sample" (sample identifier) and "ASV_X", such that each row contains the raw counts of each ASV in a given sample row.
BAC_SAM.csv + FUN_SAM.csv – Sample data tables containing information for each sample including plot, soil, and climate properties.
| Columns | Name | Description | Unit |
|---|---|---|---|
| sample.name.match | Unique sample identifier used for sequencing | NA | |
| Site | 6 letter site identifier | NA | |
| SiteSamp | Concatenated Site and Sample number (unique for each sample) | ||
| Samp | Sample number within a Site | ||
| Tissue | L = leaf, S = soil, R = root | NA | |
| sampling.day | Sample collection day | day | |
| sampling.month | Sample collection month | month | |
| sampling.year | Sample collection year | year | |
| mono.mixed | Site planting with three levels: mono = switchgrass monoculture, mixed-grass = mixed grasses, mixed-tree = switchgrass-tree intercropping | NA | |
| stand.age.yrs | Stand age in years | yrs | |
| cultivar | Switchgrass cultivar, if known | NA | |
| plotarea.m2 | Plot area in meters squared | m2 | |
| lat | Site latitude | decimal degrees | |
| lon | Site longitude | decimal degrees | |
| MAP.mm | Mean annual precipitation in mm | mm | |
| MAT.C | Mean annual temperature in degrees C | C | |
| SOM | Soil organic matter | % | |
| W.V | Soil bulk density | g/cm3 | |
| ph | Soil pH | NA | |
| watercontent | Soil moisture | g/g | |
| P | Soil P | ug/g | |
| K | Soil K | ug/g | |
| Ca | Soil Ca | ug/g | |
| Cu | Soil Cu | ug/g | |
| Mg | Soil Mg | ug/g | |
| Mn | Soil Mn | ug/g | |
| S | Soil S | ug/g | |
| Zn | Soil Zn | ug/g | |
| TIN | Soil inorganic N | ug/g | |
| perc.C | Soil %C | % | |
| perc.N | Soil %N | % | |
| mbc | Soil microbial biomass carbon | ug/g | |
| doc | Dissolved organic C | ug/g | |
| p.resin | Soil phosphate | ug/g | |
| perc.sand | Soil sand % | % | |
| perc.clay | Soil clay % | % | |
| max.height.m | Plant height in meters | m | |
| max.basalwidth.m | Can be used to calc plant basal area in meters squared | m | |
| max.basallength.m | Can be used to calc plant basal area in meters squared | m | |
| samp.lat | Plant sample latitude | decimal degrees | |
| samp.lon | Plant sample longitude | decimal degrees |
BAC_TAX.csv + FUN_TAX.csv – Table of taxonomic assignments for each ASV.
| Columns | Name | Description |
|---|---|---|
| ASV | Unique ASV identifier | |
| kingdom | Kingdom | |
| phylum | Phylum | |
| class | Class | |
| order | Order | |
| family | Family | |
| genus | Genus |
BacFunPhyloseqObjects.RData – RData file containing the full phyloseq objects for bacteria and fungi.
Leaf_bacteria_phylotree.nwk – Phylogenetic tree file in Newick format, generated from using T-BAS to align ASV 16S sequences from leaf samples to a reference database.
Leaf_fungi_phylotree.nwk – Phylogenetic tree file in Newick format, generated from using T-BAS to align ASV ITS sequences from leaf samples to a reference database.
Root_bacteria_phylotree.nwk – Phylogenetic tree file in Newick format, generated from using T-BAS to align ASV 16S sequences from root samples to a reference database.
Root_fungi_phylotree.nwk – Phylogenetic tree file in Newick format, generated from using T-BAS to align ASV ITS sequences from root samples to a reference database.
InteractionData.xlsx – File containing data for each node (i.e. ASV).
| Sheets | Name | Description |
|---|---|---|
| L80Fun | data for the fungal nodes in the leaf network, where the edge-wise variability estimate = 0.80 | |
| L80Bac | data for the bacterial nodes in the leaf network, where the edge-wise variability estimate = 0.80 | |
| R80Fun | data for the fungal nodes in the root network, where the edge-wise variability estimate = 0.80 | |
| R80Bac | data for the bacterial nodes in the root network, where the edge-wise variability estimate = 0.80 | |
| Columns | Name | Description |
| asv | node (ASV) identifier | |
| degree | degree of the node, or how many edges it shares with other nodes | |
| eigenvector | eigenvector centrality of the node, or measure of influence in the network | |
| interaction | interaction type of the node, where direct = direct interactor, keystone = diffuse interactor, within = only shares edges with nodes of the same kingdom |
NodesList.xlsx – File containing columns associated with each network and its included nodes (taxa), used for subsetting data tables to include only those taxa that remained in each network
| Sheets | Name | Description |
|---|---|---|
| bac | bacterial nodes (ASVs) included in each leaf or root network at each edge-wise variability estimate threshold | |
| fun | fungal nodes (ASVs) included in each leaf or root network at each edge-wise variability estimate threshold | |
| Columns | Name | Description |
| l_full | nodes (ASVs) included in the leaf network with no edge-wise variability estimate filter | |
| l_95 | nodes (ASVs) included in the leaf network, where the edge-wise variability estimate = 0.95 | |
| l_90 | nodes (ASVs) included in the leaf network, where the edge-wise variability estimate = 0.90 | |
| l_80 | nodes (ASVs) included in the leaf network, where the edge-wise variability estimate = 0.80 | |
| r_full | nodes (ASVs) included in the root network with no edge-wise variability estimate filter | |
| r_95 | nodes (ASVs) included in the root network, where the edge-wise variability estimate = 0.95 | |
| r_90 | nodes (ASVs) included in the root network, where the edge-wise variability estimate = 0.90 | |
| r_80 | nodes (ASVs) included in the root network, where the edge-wise variability estimate = 0.80 |
Interactors.xlsx – File containing lists of ASVs that are considered direct or diffuse (keystone) for each network and keystone threshold.
| Sheets | Name | Description |
|---|---|---|
| leaf-D | ASVs identified as direct interactors in the leaf networks | |
| root-D | ASVs identified as direct interactors in the root networks | |
| l.k75 | ASVs identified as diffuse (keystone) interactors based on the 75th percentile of degree and eigenvector in the leaf networks | |
| r.k75 | ASVs identified as diffuse (keystone) interactors based on the 75th percentile of degree and eigenvector in the root networks | |
| l.k80 | ASVs identified as diffuse (keystone) interactors based on the 80th percentile of degree and eigenvector in the leaf networks | |
| r.k80 | ASVs identified as diffuse (keystone) interactors based on the 80th percentile of degree and eigenvector in the root networks | |
| l.k90 | ASVs identified as diffuse (keystone) interactors based on the 90th percentile of degree and eigenvector in the leaf networks | |
| r.k90 | ASVs identified as diffuse (keystone) interactors based on the 90th percentile of degree and eigenvector in the root networks | |
| Columns | Name | Description |
| fun95 | fungal network from edge-wise confidence = 0.95 | |
| bac95 | bacterial network from edge-wise confidence = 0.95 | |
| fun90 | fungal network from edge-wise confidence = 0.90 | |
| bac90 | bacterial network from edge-wise confidence = 0.90 | |
| fun80 | fungal network from edge-wise confidence = 0.80 | |
| bac80 | bacterial network from edge-wise confidence = 0.80 |
Scripts and analyses
Hammer_Networks_PrepData.Rmd – R markdown script for compiling ASV information into phyloseq objects for easy loading into downstream analyses.
Hammer_Networks_Phylogenetics.Rmd – R markdown script for testing of phylogenetic signal of taxon interaction type.
Hammer_Networks_Diversity.Rmd – R markdown script for calculating alpha and beta diversity and permANOVAs of diversity as a function of plant compartment.
Hammer_Networks_SpiecEasi.Rmd – R markdown scriptR markdown script for constructing site-level ecological association networks and identifying hub taxa in merged networks (post-Cytoscape union of site networks)
Hammer_Networks_variancePartitioning.Rmd – R markdown script for conducting variance partitioning analyses and RDA for significance.
Hammer_Networks_titanEnvBiotic.Rmd – R markdown script for environmental gradient analyses (TITAN2) and the associated permANOVAs to select those environmental variables.
Note
Data files must be placed in your working directory for the analyses, or paths must be specified.
Publication information
Hammer RA, Lee MR, Kimbrel J, Stuart R, Hawkes CV (2026) Direct and diffuse cross-kingdom interactions in plant microbiome assembly. Ecology
Funding information
This work was supported by a DOE Genomic Sciences Program SFA (SCW1039) subaward from Lawrence Livermore National Lab, the Research Capacity Fund (HATCH), project award no. 7005451, from the U.S. Department of Agriculture’s National Institute of Food and Agriculture, the Foundation for Food & Agriculture Research FFAR Fellows Program RDS-0000000002, and startup funding from North Carolina State University to CVH.
Additional data – raw sequences
Sequence data are available at the NCBI Sequence Read Archive (PRJNA648664).
Eight switchgrass (Panicum virgatum L.) plants were sampled at each of 14 sites across a 462-km range in North Carolina, USA, in September and October 2018. At each plant, we collected leaves, roots, and soils immediately adjacent to the plant, which were stored on ice for transport to the lab. Subsamples were used for biogeochemical analyses or frozen at -80 °C until DNA extraction.
Statistical analyses were carried out in R v4.1.1. Phylogenetic conservation of interaction type was evaluated with Pagel’s λ using geiger v2.0.10, ape v5.6-2, and phytools v1.0-1. Cross-kingdom ecological association networks were inferred using sparse inverse covariance estimation for ecological association inference (SPIEC-EASI) with SpiecEasi v1.1.2. Variance partitioning was conducted using vegan v2.6-4, and shifts in ASV abundances along environmental gradients were characterized using TITAN2 v2.4.3.
Sequence data are available on the NCBI Short Read Archive under BioProject PRJNA648664.