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553 results for “soil communities”
Dead or alive; or does it really matter? Level of congruency between trophic modes in total and active fungal communities in High Arctic soil.
<p>These are the rDNA and rRNA fragments of Internal transcribed spacer 2 (ITS2) extracted from snow fence experiment in Adventdalen, Svalbard. </p> <p>This is a dataset described in Wutkowska et al., (2019), 'Dead or alive; or does it really matter? Level of congruency between trophic modes in total and active fungal communities in High Arctic soil.', published in Frontiers in Microbiology</p> <p>All other corresponding data (for splitting libraries, environmental parameters etc.) can be found here: https://github.com/magdawutkowska/Dead_or_alive</p> <p> </p>
Plant Communities at the Eden Project, UK, derived from soil eDNA
<p>The project seeks to understand the potential for the use of eDNA collected from soil to characterise plant communities. To do so, soils were sampled at the Eden Project in Cornwall UK, within the two covered biomes where we have a good understanding of the structure and composition of plant communities (further quantified with above ground plant coverage inventories). 32 plots were established across 10 different plant assemblages, each of which experiences subtle differences in soil chemistry and microclimate. Each plot consists of a 2 x 2 m quadrat, with four soil aggregates collected at each corner. </p> <p>eDNA was then extracted and amplified following the methods detailed in Zinger et al. (2016) and Donald et al. (2021). The primers used targeted the P6 loop of thechloroplastic trnL intron [primer_fwd: GGGCAATCCTGAGCCAA, primer_rev: CCATTGAGTCTCTGCACCTATC] (Taberlet et al. 2007). 16 Extraction, 54 Sequencing, and 16 PCR controls are included so as to account for potential errors generated during the processing of samples, with a mock community (4 positive controls) of 10 known plant sequences also included to guide filtering thresholds. PCR products were pooled and sequencing libraries were constructed using the Illumina TruSeq NanoPCRFree kit following the supplier’s instructions (Illumina Inc., San Diego, California, USA), except that the ligation product was not PCR amplified to limit tag-jump biases (Taberlet et al 2018). The libraries were then sequenced on an Illumina Hiseq platform (San Diego, CA, USA).</p> <p>Sequencing was conducted by the GenoToul bioinformatics platform (Toulouse, France), with the OBITOOLS package (Boyer et al. 2016). Here, the produced sequence data was processed using the following steps. First, ‘illuminapairedend’ was used to assemble paired-end reads. This algorithm is based on an exact alignment algorithm that considers the quality scores at all positions during the assembly process. Subsequently, we used the ‘ngsfilter’ command to identify and remove the primers and tags on each read, and assign reads to their respective samples (NGS filter file provided: <strong>ngsfilter_TRNL_PLANTS_EDEN_PROJECTb.txt</strong>). This program was used with its default parameters tolerating two mismatches for each of the two primers and no mismatch for the tags. Following this, sequencing reads were dereplicated using the ‘obiuniq’ command. The produced <strong>data.uniq.fasta</strong> file is supplied here. Sequences were then further filtered to remove sequences of low quality (containing Ns or with paired-end alignment scores below 50), and sequences represented by only one read (singletons) using the ‘obigrep’ command. To remove PCR/sequencing errors as well as intraspecific variability, we built OTUs (Operational Taxonomic Units) using the ‘sumaclust’ clustering algorithm (Mercier et al. 2013), which considers the most abundant sequence of each cluster as the cluster representative. OTUs were set at a sequence similarity threshold of 95%. To assign a taxon to plant OTUs, we built a reference sequence database using the ecoPCR programme (Ficetola et al. 2010) on the European Molecular Biology Laboratory (EMBL; release 141). OTUs were then assigned a taxonomy, using OBITOOL’s ecotag programme (Boyer et al. 2016), which performs a global alignment of each OTU sequence (the query) against each reference. The reference taxon assigned to each OTU corresponds to the Last Common Ancestor of all the best-match sequences for the query. </p> <p><br> Datasets were subsequently filtered to remove contaminants as well as artefacts such as PCR chimeras and remaining sequencing errors, using routines implemented in the metabaR R package (Zinger et al 2021), in R version 3.6.1 (R Development Core Team, 2013). The filtering process consisted of four steps: (i) a negative control-based filtering. OTUs whose maximum abundance was found in extraction/PCR negative controls were removed from the dataset, as they were likely to be reagent/aerosol contaminants, better amplified in the absence of competing DNA fragments as it is the case in biological samples. (ii) a reference-based filtering. OTUs which are too dissimilar from sequences available in reference databases are potential chimeras generated during sequencing and amplification. In this study, we chose to set similarity thresholds at 100%. (iii) an abundance-based filtering. This procedure targets incorrect assignment of a few numbers of sequences corresponding to true OTUs occurring to the wrong sample, a phenomenon called “tag-switching”. It consists in setting OTUs abundances to 0 in samples where their abundance represents < 0.03% of the total OTU abundance in the entire dataset. (iv) Finally, we conducted a PCR-based filtering by considering any PCR reaction that yielded less than 1000 reads as non-functional, and removed them from the dataset. The script used for implementing this is provided (<strong>metabaR_Eden_Plants_100sim.html)</strong>, with sequence data processed to remove contaminants, OTUs of low taxonomic resolution, and PCRs with too low a read count. The clean data is provided (<strong>eden_plant_postclean_100sim.rds</strong>).</p> <p>References:</p> <p>Boyer, F. <em>et al.</em> (2016) ‘obitools: a unix-inspired software package for DNA metabarcoding’, <em>Molecular Ecology Resources</em>, 16(1), pp. 176–182. doi:<a href="https://doi.org/10.1111/1755-0998.12428">10.1111/1755-0998.12428</a>.</p> <p>Donald, J. <em>et al. (2021) '</em>‘Multi-taxa environmental DNA inventories reveal distinct taxonomic and functional diversity in urban tropical forest fragments.‘ <em>Global Ecology and Conservation</em> 29 (2021): e01724.</p> <p>Mercier, C. <em>et al.</em> (2013) ‘SUMATRA and SUMACLUST: fast and exact comparison and clustering of sequences’, in <em>Programs and Abstracts of the SeqBio 2013 workshop. Abstract</em>. Citeseer, pp. 27–29.</p> <p>Taberlet, P. <em>et al.</em> (2007) ‘Power and limitations of the chloroplast trn L (UAA) intron for plant DNA barcoding’, <em>Nucleic Acids Research</em>, 35(3), pp. e14–e14. doi:<a href="https://doi.org/10.1093/nar/gkl938">10.1093/nar/gkl938</a>.</p> <p>Taberlet, P. <em>et al.</em> (2018) <em>Environmental DNA: For Biodiversity Research and Monitoring</em>. Oxford University Press.</p> <p>Team, R.C. (2013) <em>R: A language and environment for statistical computing</em>. Vienna, Austria.</p> <p>Zinger, L. <em>et al.</em> (2016) ‘Extracellular DNA extraction is a fast, cheap and reliable alternative for multi-taxa surveys based on soil DNA’, <em>Soil Biology and Biochemistry</em>, 96, pp. 16–19.</p> <p>Zinger, L. et al. (2021) ‘metabaR: An r package for the evaluation and improvement of DNA metabarcoding data quality’, Methods in Ecology and Evolution. DOI: <a href="https://doi.org/10.1111/2041-210X.13552">https://doi.org/10.1111/2041-210X.13552</a></p>
Data for: Heavy metal pollution impacts soil bacterial community structure and antimicrobial resistance at the Birmingham 35th Avenue Superfund Site
<p>The data in this archive are the results of a study on the impact of heavy metals (HMs) on the soil microbiota of an urban Superfund site in Alabama. HMs are known to modify bacterial communities both in the laboratory and in situ. Consequently, soils in HM-contaminated sites such as the U.S. Environmental Protection Agency (EPA) Superfund sites are predicted to have altered ecosystem functioning, with potential ramifications for the health of organisms, including humans, that live nearby. Further, several studies have shown that heavy metal-resistant (HMR) bacteria often also display antimicrobial resistance (AMR), and therefore HM-contaminated soils could potentially act as reservoirs that could disseminate AMR genes into human-associated pathogenic bacteria. To explore this possibility, topsoil samples were collected from six public locations in the zip code 35207 (the home of the North Birmingham 35th Avenue Superfund Site) and in six public areas in the neighboring zip code, 35214. 35027 soils had significantly elevated levels of the HMs As, Mn, Pb, and Zn, and sequencing of the V4 region of the bacterial 16S rRNA gene revealed that elevated HM concentrations correlated with reduced microbial diversity and altered community structure. While there was no difference between zip codes in the proportion of total culturable HMR bacteria, bacterial isolates with HMR almost always also exhibited AMR. Metagenomes inferred using PICRUSt2 also predicted significantly higher mean relative frequencies in 35207 for several AMR genes related to both specific and broad-spectrum AMR phenotypes. Together, these results support the hypothesis that chronic HM pollution alters the soil bacterial community structure in ecologically meaningful ways and may also select for bacteria with increased potential to contribute to AMR in human disease.</p>
Data from: Subtle responses of soil bacterial communities to corn-soybean-wheat rotation
<p>Crop rotational diversity can improve crop productivity and soil health and boost soil microbial diversity. This research hypothesized that a three-year rotation of corn-soybean-wheat (CSW), compared to a two-year corn-soybean (CS) rotation, would result in a more diverse and more complex soil bacterial community, together with a greater abundance of beneficial bacteria. This was evaluated in a replicated experiment established in 2013 at two locations in Ohio (USA). The soil bacterial communities under soybean were compared between CS and CSW, at both studied sites, in 2018 and 2019, through 16S rDNA amplicon metabarcoding. </p>
Microbial community composition of earthworm-invaded and earthworm-free soils of the Canadian boreal forest
<p>Earthworm invasion in North American forests has the potential to greatly impact soil microbiomes by altering soil physicochemical properties. We characterized and compared microbial communities of earthworm-invaded and non-invaded soils in previously described sites across three major soil types found in the Canadian boreal forest using phospholipid fatty acid (PLFA) analysis and metabarcoding of the 16S rRNA gene (bacteria and archaea) and ITS2 region (fungi).</p>
Dataset for the article "Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities"
<p>The following are data and code used for statistical analysis and figure plotting in the manuscript</p> <p>Gorka et al. (2023) "Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities", Soil Biology and Biochemistry</p> <p>It contains the following files:</p> <p>1. Pure lipid standard data</p> <ul> <li>Total ion chromatogram (TIC) area data (<strong>area.csv</strong>)</li> <li>Assignment of lipids that the measured fatty acids originate from (<strong>LipidClass.csv</strong>)</li> <li>An R script reproducing the calculations and plotting for Fig. 2 and Fig. S1 (<strong>pure_lipids.R</strong>)</li> </ul> <p>2. Microbial pure culture fatty acid data data</p> <ul> <li>TIC area data of the PLFA, NLFA, and GLFA data from pure culture extracts (<strong>area.csv</strong>)</li> <li>Files needed for calculating the data and assigning taxonomic groups in the R code (<strong>weights.csv</strong>, <strong>C_atoms.csv</strong>, <strong>species_list.csv</strong>)</li> <li>An R script reproducing the calculations and plotting for Fig. 3, Fig. 4, Fig. S2, and Fig. S3 (<strong>pure_cultures.R</strong>)</li> </ul> <p>3. Soil fatty acid data</p> <ul> <li>Absolute abundance data in nmol C g<sup>-1</sup> dry weight of the PLFA, NLFA, and GLFA data from soil extracts (<strong>nmolC.csv</strong>)</li> <li>Taxonomic group assignments of fatty acids needed to run the R code (<strong>phylum.csv</strong>)</li> <li>An R script reproducing the calculations and plotting for Fig. 5, and Fig. S4 (<strong>soil.R</strong>)</li> </ul>
Dataset for Phosphite as an Engineered Niche for Pseudomonas veronii in a Synthetic Soil Bacterial Community
<p>Files containing the source data and analyses used in the manuscript "Phosphite as an Engineered Niche for <em>Pseudomonas veronii </em>in a Synthetic Soil Bacterial Community".</p> <p> </p> <p>Clara Bailey (1), Philip Gwyther (2), Senka Čaušević (2), Brandon L. Greene (1), and Jan Roelof van der Meer (2)</p> <p>1) Department of Chemistry and Biochemistry, University of California, Santa Barbara, Santa Barbara, California, United States</p> <p>2) Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland</p> <p> </p> <p>This dataset contains 16S rRNA gene amplicon sequencing data (in the form of an abundance table, "abund.csv" and combined with CFU counts in "abund_cfu.csv"), toluene quantification data, and CFU counts. All data analysis, statistical tests, and generated figures are contained in the relevant .R script. Refer to README files for data tables as well as the README section at the header of the R script. The dataset has been updated from version 1 to include manuscript revisions, and updated calculations and figures. </p> <p> </p>
Drivers of plant diversity, community composition, functional traits and soil processes along an alpine gradient in the central Chilean Andes
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Data from: Fungal energy channeling sustains soil animal communities across forest types and regions
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Different facets of bacterial and fungal communities drive soil multifunctionality in grasslands spanning a 3,500 km transect
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Data for: Soil legacy effects of plants and drought on aboveground insects in native and range-expanding plant communities
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Unraveling community adaption and survival strategy of soil microbiome under vanadium stress in nationwide mining environments
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Raw data of the microarthropods and decomposition from: Effects of microarthropod density on soil fungal community composition in nutrient-poor ecosystems
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Impacts of an omnivorous ungulate on plant communities and soil organic carbon
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Data from: Plant, insect, and soil microbial communities vary across brome invasion gradients in northern mixed-grass prairies
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Metadata from: Rhizosphere bacteria and fungi are differentially structured by host plants, soil mineralogy and ectomycorrhizal communities in the Alaskan tundra
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Data for: Heavy metal pollution impacts soil bacterial community structure and antimicrobial resistance at the Birmingham 35th Avenue Superfund Site
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Microbial community composition of earthworm-invaded and earthworm-free soils of the Canadian boreal forest
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Data from: Subtle responses of soil bacterial communities to corn-soybean-wheat rotation
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Plant management but not fertilization mediates soil carbon emission and microbial community composition in subtropical Eucalyptus plantations
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