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188 results for “Microbial diversity”
Soil visible–near infrared (vis–NIR) spectra for the Biomes of Australian Soil Environments (BASE) soil microbial diversity database
<p>Visible–near infrared spectra of 695 soil samples collected in the Biomes of Australian Soil Environments (BASE) soil microbial diversity project (Bissett et al., 2016). The spectra represent reflectance values from 2151 wavelengths that range from 350 nm to 2500 nm with a 1 nm interval. The dataset has unique sample identification numbers and the date of sampling, which can be related to the BASE (Australian Microbiome) database (https://data.bioplatforms.com/organization/australian-microbiome)</p>
Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population
<h1>Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population</h1> <h1> </h1> <p>These files contain data on bacteria present in the guts of wild great tit (Parus major) obtained from faecal samples and sequenced using Illumina MiSeq. These data resulted from an experiment which provided supplementary mealworms at the nest during the breeding season at number of woodland sites in Cork, Ireland. Approximately half of these nests were given mealworms covered in a freeze dried bacterial powder containing the bacteria Lactobacillus kimchicus, which had been isolated from great tit faeces from the previous season. This treatment aimed to disrupt the gut microbiota of the treatment birds in order to provide evidence for the gut microbiotas role in birds health and fitness. Included here are the 3 elements necessary to create a 'phyloseq object' containing the sample metadata, ASV (Amplicon Sequence Variant) count table and a taxonomy table. The metadata file includes the alpha diversity scores for each individual. The data include all negative control samples taken during sample collection and library preparation, which were removed before the main analyses. All analyses, except for the beta-diversity analyses, were conducted in R. All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p> <h2> </h2> <h2>## Description of the data and file structure </h2> <p>Taxonomy, ASV and metadata files required to create a phyloseq object in R. metadata.csv file contains data on individual birds (i.e. individual samples). The metadata includes descriptions of the bird itself and it's environment, namely:</p> <ul> <li>Rownames: unique sample ID for each sample, corresponds with asvTable.csv. </li> <li>Nest: unique identifier for the nest box associated with the bird being sampled. </li> <li>Sample.ID: unique identifier for the faecal sample or control sample.</li> <li>Bird.ID: Identity of the bird the sample came from, note some individuals sampled twice so some bird.ID's may reoccur in metadata with different Sample.ID.</li> <li>Date: Date the sample was taken dd/mm/yyyy.</li> <li>Day: Date the sample was taken, in days since 1st March.</li> <li>Ring.Mark: British Trust for Ornithology (BTO) metal ring ID where applicable. Birds only ringed at D15 so some young birds do not have IDRings.</li> <li>Site: ID of woodland site that bird was sampled at.</li> <li>Chick.LetterID: ID letter differentiates between different birds from the same nest. Either 'A'-'F' for nestlings, 'Fe' for females or 'M' for males.</li> <li>Age.code: BTO age code.</li> <li>Age.category: Age category that bird is in. D8 = 8 days post hatching, D15 = 15 days post hatching, adult = 1+ years post hatching.</li> <li>Sex: Bird's sex, only determined for adult birds. Fe = Female, M = Male.</li> <li>Wing_mm: Wing length in mm.</li> <li>Tarsus_mm: minimum tarsus length of bird in mm.</li> <li>Weight_g: bird's weight in grams.</li> <li>Faecal.Sample: bird's age at sampling.</li> <li>newRing: whether bird was fitted with a new BTO ring. Only relevant to adults.</li> <li>Treatment: the experimental treatment group that the bird was in. Either 'Treatment' when nest given L. kimchicus treated mealworms or 'Control' when nest given plain mealworms.</li> <li>Notes: field notes.</li> <li>Main.sample: indicates whether this sample was the main sample to be used for analysis, an alternative sample taken as a backup.</li> <li>Plate: the ID of the PCR plate which the sample was amplified on.</li> <li>Azenta_noPeriod: sample ID given to sequencing facility without special characters. Corresponds to fastq files and ASV table counts.</li> <li>Qubit_prePool: samples qubit score before pooling.</li> <li>Date_extracted: date the sample was extracted on dd/mm/yyyy.</li> <li>SampleType: whehther the sample was a 'main' sample intended for downstream analysis, a 'control' sample for detecting contamination during library preparation, a 'duplicate' for detecting PCR issues, a 'label_error' where sample was suspected of being mislabelled at some point, a 'repeat' sample intended to detect errors or issues, a 'contam' sample which was suspected of being contaminated, a 'common' sample used across different PCR plates to detect issues. Extraction_notes: notes regarding the DNA extraction of the sample. </li> <li>LibPrep_notes: notes regarding the library preparation of the sample.</li> <li>Ring.Mark.lab: the ring or sample ID written on the sample tube, recorded to help detect mislabelling.</li> <li>Post_lab_notes: notes regarding issues found post sequencing.</li> <li>NumberOfReads: number of sequence reads associated with the sample. </li> <li>DistanceToEdge: distance between nest and woodland edge in metres. </li> <li>BroodSize.D8: number of nestlings in the nest at day-8 post hatching. </li> <li>BroodSize.D15: number of nestlings in the nest at day-15 post hatching.</li> <li>firstEggLayDate: Date the first egg in the clutch was laid, in days since 1st March.</li> <li>lastEggLayDate: Date the last egg in the clutch was laid, in days since 1st March.</li> <li>Observed: number of unique ASV's (or taxa) detected in the sample.</li> <li>Chao1: Chao1 diversity of the sample.</li> <li>Shannon: Shannon diversity of the sample.</li> </ul> <p>The file 'taxonomy.csv' contains the taxonomic breakdown of each bacterial Amplicon Sequence Variant (ASV) found in the dataset from Phylum to Species. Obtained by using the Naive Bayes Classifier against the Silva (v138) taxonomic database.</p> <p>The file 'asvTable.csv' contains counts of each amplicon sequence variant's occurrence for each individual sample. Samples are rows and taxa are columns.</p> <p> </p> <h2>Sharing/Access information </h2> <p>All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p>
The impact of beech deadwood on soil properties and microbial diversity
<p><span><span>Our research is an attempt to determine the role of decaying wood in shaping the properties of forest soils in mountain ecosystems.</span></span><span><span> </span></span></p>
Elevation Gradient (EG) Soil Microbial diversity FAME and TRFLP data
Soil fungal communities respond to multiple abiotic and biotic factors that change along elevation gradients. The limited information available on fungi and microbial processes along elevation gradients is primarily from temperate areas and very few from tropical regions. This study documents changes in fungal and bacterial diversity, and abundance and composition of microbial functional groups along a subtropical elevation gradient. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Canopy Trimming Experiment (CTE) Microbial diversity DNA data
Hurricanes are common disturbances in the Caribbean region that affect tree distribution, species diversity and biomass in forests. Little is known of how microbial communities in soil and litter are affected by natural or anthropogenic disturbances. The objective of our study was to determine the relative abundance and diversity of microorganisms in leaf litter at different stages of decomposition, and the effect of canopy opening and debris addition or removal. Results: Leaf mass loss was slowest in the treatment with canopy trimming and debris removal. Canopy opening was associated with lower litter moisture, lower fungal connectivity between litter layers and slower mass loss after three months. Addition of green leaves increased moisture, and frequently accelerated mass loss of the senesced leaves below them at 17, 28 and 40.5, but not 53 weeks. After 28 weeks, mass loss showed a significant treatment interaction, and was concordant with fungal connectivity between litter cohorts. TRFLP profiles of the 16S rDNA digested with MnlI and fungal ITS digested with HaeIII shows that the microbial communities at 17, 28 and 53 weeks were highly divergent among treatments (Sorensen index of similarity). In comparisons of green versus senesced leaves within treatments, bacterial communities’ differed somewhat, fungal communities differed strongly, but mass loss did not differ. Conclusions: Microbial community changes through time can be related to microclimate and the availability of labile compounds. Fungi appeared to control the succession of microorganisms in decomposing leaves. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute o
Geochemical, physicochemical, and genomic data from a continental-scale survey of microbial diversity in Antarctic soils (2003-2023)
This data package offers comprehensive insights into Antarctic soil microbial diversity and composition. From 2003 to 2023, a total of 186 samples were collected from diverse locations spanning the Antarctic Peninsula to East Antarctica, representing a wide range of environmental gradients and climatic conditions. Soils were stored at -20°C to preserve their integrity for downstream analyses. This data package integrates cultivation-independent sequencing of prokaryotic and fungal communities alongside a robust cultivation-dependent culture collection to enable direct comparisons across microbial diversity assessment methods. Accompanying geochemical, physicochemical, and environmental parameters provide critical context for biogeographical analyses, offering a valuable resource for studying microbial adaptations and community dynamics in extreme Antarctic environments.
Soil pH, developmental stages and geographical origin differently influence the root metabolomic diversity and root-related microbial diversity of Echium vulgare from native habitats
<p>R Studio codes and ASV table used to analyze the microbiome data of our Echium vulgare microbial ecology experiment. </p>
Experimental erosion of microbial diversity decreases soil CH4 consumption rates
<p>Biodiversity-ecosystem functioning (BEF) experiments have predominantly focused on communities of higher organisms, in particular plants, with comparably little known to date about the relevance of biodiversity for microbially-driven biogeochemical processes. Methanotrophic bacteria play a key role in Earth’s methane (CH<sub>4</sub>) cycle by removing atmospheric CH<sub>4</sub> and reducing emissions from methanogenesis in wetlands and landfills. Here, we used a dilution-to-extinction approach to simulate diversity loss in a methanotrophic landfill cover soil community. Replicate samples were diluted 10<sup>1</sup> to 10<sup>7</sup>-fold, and pre-incubated under a high CH<sub>4</sub> atmosphere for the microbial communities to recover to approximately equal size. Then, the samples were incubated for 86 days at constant or diurnally-cycling temperature. Our hypotheses were that (1) CH<sub>4</sub> consumption would decrease as methanotrophic diversity was lost, and that (2) this effect would be more pronounced under variable environmental conditions (here: variable temperature). We followed net CH<sub>4</sub> consumption by gas chromatography. Microbial community composition was determined four times by DNA extraction and sequencing of amplicons specific to methanotrophs and bacteria (pmoA and 16S gene fragments). We found that the richness of operational taxonomic units (OTU) of methanotrophic and non-methanotrophic bacteria decreased approximately linearly with <em>log</em>-dilution. CH<sub>4</sub> consumption decreased with the number of taxonomic units lost. This effect was independent of community size, which we determined by quantitative PCR, and consistent over the study period. The temperature treatment (constant vs. cycling temperature) did not affect any of these results. The diversity effects we found occurred in relatively diverse communities, challenging the notion of high functional redundancy mediating high resistance to diversity erosion in natural microbial systems. The effects we report resemble the ones for higher organisms, suggesting that BEF-relationships are universal across taxa and spatial scales.</p>
Plumes and Blooms: Microbial eukaryote diversity and composition
These are amplicon sequencing data collected during Plumes and Blooms (PnB) cruises conducted from March, 2011, through September, 2014. The V9 hypervariable region of the 18S rRNA gene derived from microbial eukaryotic communities was amplified and sequenced from 345 discrete seawater samples. Sample collection and laboratory methods are described in Catlett et al. 2020 and Catlett et al. in review. Bioinformatic and data manipulation methods follow those employed in Catlett et al. in review. The data are provided in two tables: one includes amplicon sequence variant (ASV) sequences and relative sequence abundances for each sampling event, and the other includes ASV taxonomy predictions for each ASV sequence. References: Catlett, D., P. G. Matson, C. A. Carlson, E. G. Wilbanks, D. A. Siegel, and M. D. Iglesias‐Rodriguez. 2020. Evaluation of accuracy and precision in an amplicon sequencing workflow for marine protist communities. Limnol. Oceanogr.: Methods. 18(1): 20-40. https://doi.org/10.1002/lom3.10343. Catlett, D., D. A. Siegel, P. G. Matson, E. K. Wear, C. A. Carlson, T. S. Lankiewicz, and M. D. Iglesias‐Rodriguez. In review. Integrating phytoplankton pigment and DNA meta-barcoding observations to determine phytoplankton community composition in the coastal ocean. Limnol. Oceanogr.
UV radiation accelerates litter decomposition in a valley-type savanna by enhancing microbial community diversity and function
<p><span>We present the data of the study by Gao et al. (202</span><span>4</span><span>): <a name="_Hlk179470571"></a><a name="OLE_LINK42"></a><strong><span>UV radiation accelerates litter decomposition in a valley-type savanna by enhancing microbial community diversity and function</span></strong></span><span>.</span><span> The excel file (Raw Data) includes the following sheets: 1- Radiation (w·m<sup>-2</sup>) variation of UV-A and UV-B during the experimental period. 2- Decay constants (<em>K</em>, yr<sup>−1</sup>) and changes in the mass loss rate of litter under different UV conditions during the experimental period<span>. 3- </span>The content of lignin, cellulose, C, N and P of litter under different UV conditions during the experimental period<span>. 4-</span></span><span> </span><span>16S ASVs under different UV conditions<span>. 5-</span></span><span> </span><span>ITS ASVs under different UV conditions.</span></p>
Native plant diversity creates microbial legacies that either promote or suppress non-natives, depending on drought history
<p>High-diverse native plant communities resist non-native plants more strongly than low-diverse communities, in part through resource competition. Yet, the role of soil biota is largely unknown, although non-native plants interact with soil biota. Here, we tested the responses of non-native plants to soil conditioned by different native plant diversities. We applied well-watered and dry treatments in the conditioning and response phases to explore the effects of historical and contemporary environmental stresses. Historical water conditions determined the effects of native diversity via soil biota on responding non-natives grown in well-watered environments. Non-native growth decreased with native species richness for well-watered soil inocula but increased for dry soil inocula. However, non-native growth in dry environments did not depend on conditioning native species richness of soil inocula. We provide a new understanding of mechanisms behind diversity-invasibility relationships and demonstrate that temporal variation in environmental stress shapes relationships among native plant diversity, soil biota, and non-native plants.</p>
Figure 9 in Microbial diversity of ticks and a novel typhus group Rickettsia species (Rickettsiales bacterium Ac37b) in Inner Mongolia, China
Figure 9. Phylogenetic tree of Rickettsiales bacterium Ac37b, Rickettsia bellii, Rickettsia raoultii, Anaplasma and Coxiella in ticks based on neighbor-joining (NJ) modeling; only values higher than 60 were added to the tree branches. (a) Phylogenetic tree of Rickettsiales bacterium Ac37b identified in Inner Mongolia; the 16S rRNA gene sequences obtained in this study are marked with black squares (1320 bp) and triangles (1430 bp). (b) Phylogenetic tree of Rickettsia bellii identified in Inner Mongolia; the 16S rRNA gene sequences obtained in this study are marked with black squares (1109 bp). (c) Phylogenetic tree of Rickettsia raoultii identified in Inner Mongolia; the 16S rRNA gene sequences obtained in this study are marked with black squares (855 bp). (d) Phylogenetic tree of Anaplasma identified in Inner Mongolia; the 16S rRNA gene sequences obtained in this study are marked with black squares (1455 bp) and triangles (547 bp). (e) Phylogenetic tree of Coxiella identified in Inner Mongolia; the 16S rRNA gene sequences obtained in this study are marked with black squares (1463 bp).
Figure 6 in Microbial diversity of ticks and a novel typhus group Rickettsia species (Rickettsiales bacterium Ac37b) in Inner Mongolia, China
Figure 6. PCoA of β-diversity measures for twelve groups. Weighted UniFrac PCoA graph showing PC1, which accounts for 47.46% of variation, and PC2, which accounts for 28.93% of variation. Different colored dots represent different regions and species.
Figure 5 in Microbial diversity of ticks and a novel typhus group Rickettsia species (Rickettsiales bacterium Ac37b) in Inner Mongolia, China
Figure 5. (a) Clustering tree analysis by linear discriminant analysis effect size (LEfSe). (b) Histogram of LDA analysis.
Figure 4 in Microbial diversity of ticks and a novel typhus group Rickettsia species (Rickettsiales bacterium Ac37b) in Inner Mongolia, China
Figure 4. Alpha diversity measures for Dermacentor nuttalli and Ixodes persulcatus in four areas. (a) Shannon's index. (b) Simpson's index.
Figure 7 in Microbial diversity of ticks and a novel typhus group Rickettsia species (Rickettsiales bacterium Ac37b) in Inner Mongolia, China
Figure 7. Venn diagram for cluster analysis of OTUs between Dermacentor nuttalli and Ixodes persulcatus (genus level).
Figure 3 in Microbial diversity of ticks and a novel typhus group Rickettsia species (Rickettsiales bacterium Ac37b) in Inner Mongolia, China
Figure 3. Microbe composition of different regions and species. (a) Top 10 microbial components at the genus level. (b) Top 12 microbial components at the species level.
Figure 2. Shannon–Wiener curve. X in Microbial diversity of ticks and a novel typhus group Rickettsia species (Rickettsiales bacterium Ac37b) in Inner Mongolia, China
Figure 2. Shannon–Wiener curve. X-axis: amount of sequencing data; Y-axis: corresponding Shannon diversity index.
Fig. 8 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 8 | Total microbial diversity across vertebrate hindguts and within multi- plebodysites of fish. a Hindgutmicrobiotasamplesfrom 569 speciesof vertebrates were rarified to 5000 reads and unique or shared ASVs determined for each class. b The percentage of unique ASVs only found in a given class (not shared in other classes) as compared to the total ASVs within that class. c Rarefaction of cumulative gamma diversity as a function of unique vertebrate species. Included is a single fish species, S. japonicus, sampled over 3 years "black dots" and the unrarefied FMP samples which had detectable bacteria in all four body sites (gill, skin, midgut, and hindgut).d Gammadiversity of 68 fishspeciesacrossfour bodysites.e Percentageof unique ASVs associatedwitha given bodysiteacrossthe 68 fish species.f Rarefaction curve of increasing gamma diversity (inclusive of four body sites) as a function of increasing fish species. ASV amplified sequence variant, 5k 5000.
Fig. 7 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 7 | Microbial source tracking analysis. a Microbial sources of 60 sea water (blue circle) samples taken from 30 unique sampling stations from two timepoints are distributed on a 10 km transect from Torrey Pines beach to Mission Bay. Microbial sources of 108 marine sediment samples (red stars) from San Diego coastalenvironmentincludes 60 paired samples (samelocationsas seawater) from the same 10 km transect along with 58 samples from the various reef habitats near La Jolla. Geographic data presented using ArcGIS. b Sourcetracker2 analysis of likely sources for the four body sites of the fish comparing contributions of beach sand, marine sediment, sea water, and "unknown". Unknown refers to microbes from an unknown source which could include diet and other animals or locations not sampled. c Specific microbial contributions of sea water to the four mucosal body sites and d specific microbial contributions of marine sediment to the four mucosalbodysites b–d: distributionisin medianand interquartilerange.Statistical differences determined using non-parametrictesting Kruskal–Wallistest with 0.05 FDR Benjamini–Hochberg. e Proportion of microbes (distribution is in median and interquartile range) likely originating from the sea water vs. sediment for each unique body site (sea water vs. sediment pairwise comparison for each body site using Mann–Whitney test p <0.05).f Spearmanrho valuesfromcomparisons ofthe ratio of sea water "SW" and marine sediment "SED" against various continuous fish life history metadata variables for each unique body site (Spearman correlation p <0.05). g Comparison of the SW:SED ratios across the habitats from which the fish live. Comparisons performed on each unique body site (Kruskal–Wallis test, p <0.05). *p <0.05, **p <0.01, ***p <0.001, ****p <0.0001, ASV amplified sequence variant ~unique sub-Operational Taxonomic Unit, SD standard deviation, MG midgut, HG hindgut, KW Kruskal–Wallis test statistic "H", IQR inter quartile range, SW sea water.
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International Brain Laboratory public data
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OpenNeuro
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