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25 results for “Microbial Biology”
Physical soil characteristics, microbial community composition, extracellular enzymatic activity, biologically based phosphorus (BBP) pools, and available phosphorus from two soil depths, four microhabitats, and four landforms at the Jornada Experimental Range, 2021.
This dataset contains physical soil characteristics, PLFA based microbial community composition, extracellular enzymatic activity, nitrate and ammonium activity, and phosphorus availability in various phosphorus pools (Biologically Based Phosphorus, potassium sulfate, Olsen-P). Soils were collected from two depths (0-2cm, 2-30 cm), four microhabitats (grass, shrub, biocrust, interspace), and four landforms (alluvial flat, alluvial fan remnant, erosional scarplet, fan piedmont – see coordinates) within the Jornada Experimental Range in July 2021 to answer questions about how these variables change across these spatial scales in drylands. This project was a collaboration between researchers at New Mexico State University and The University of Texas at El Paso as part of the Drylands Critical Zone Thematic Cluster within the Critical Zone Network. This dataset is complete.
Spectral and biological characteristics of microbial mats and mosses across Fryxell Basin, Taylor Valley, Antarctica (2018-2019)
An intensive field campaign was conducted during the 2018-2019 austral summer to assess spectral and biological characteristics of multiple microbial mat types, as well as mosses, across nine ephemeral glacial meltwater streams in the Fryxell Basin of Taylor Valley, located in the McMurdo Dry Valleys region of Antarctica. In addition to biological sample collection, hyperspectral visible/near-infrared (VNIR) measurements were conducted using an ASD FieldSpec4 spectrometer, corrected and averaged across each mat, moss, and soil type, and downsampled to the multispectral resolution of the WorldView-2 satellite. This package contains a detailed sample archive, associated images, biological characteristics that include ash free dry mass (AFDM), chlorophyll-a, and pigment concentrations, as well as hyperspectral and multispectral reflectance spectra.
Archived plant, soil, water and microbial samples at the Kellogg Biological Station, Hickory Corners, MI (1988 to 2016)
Dataset AbstractPlants, soil, water and microbial samples are archived. Small quantities are available for research by contacting: Plant and Soil Samples — Stacey Vanderwulp Water Samples — Steve Hamilton Microbial Samples — Tom Schmidt original data source http://lter.kbs.msu.edu/datasets/55
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.
Fig. 5 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 5 | Biological and life history drivers of mucosal microbiota in diverse sampling of marine fish from Southern California. a Multivariate analysis of biological and life history parameters evaluated using unweighted and weighted normalized UniFrac distances. Statistical significance (PERMANOVA p = 0.001) indicated by yellow blocks (left) and effect size (right). All samples compared together (all) along with individual sample types (gill, skin, midgut, hindgut). b Impact of trophic level on similarity between midgut and hindgut (within a species) (linear model:p p value, mslope,dottedlinesare 95% confidence interval). F-Stat test statistic used in PERMANOVA analysis, all row names in a are metadata column names used in the analysis, MG midgut, HG hindgut, Gen. Weighted UniFrac generalized weighted UniFrac.
Fig. 6 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 6 | Evidence forphylosymbiosis across fishbody sites. Effectof evolutionary distance (low divergence time indicates a short branch length or similar fish species) of all fish compared to a skin unweighted UniFrac distance, b gill generalized weighted UniFrac distance, c hindgut generalized weighted UniFrac distance. Comparisons performed using Mantel test with multiple testing by FDR. Divergence time between fish species calculated using timetree.org. Gen. Weighted UniFrac generalized weighted UniFrac.
Fig. 3 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 3 | Alpha diversity and biomass comparisons across ecological and biolo- gical gradients in marine fish. Comparison of microbial diversity a "Chao1", b "Faith's Phylogenetic Diversity", c "Shannon", or d microbial biomassacrossbody site (gill, skin, midgut, and hindgut). Distributions in "red" are median with inter- quartile range. Statistical differences determined using non-parametric testing Kruskal–Wallistest with 0.05 FDR Benjamini–Hochberg. Further testing computed for each unique body site for a variety of biological and ecological metadata categories. Metadata whichis e categorical istestedusing Kruskal–Wallis f whereas numeric metadata tested using Spearmancorrelation. Onlysignificant associations are represented in e (Kruskal–Wallis p <0.05) and f (Spearman p <0.05). KW or KW stat "H" test statistic from Kruskal–Wallis test, MG midgut, HG hindgut, Faith PD Faith's Phylogenetic Diversity metric, GI:TL gastrointestinal length to fish total length "ratio", TL total length of fish.
Fig. 4 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 4 | Associations between fishmass and collection location as measuredby and d distance from shore with gill microbial biomass. e Comparison of fish mass distancefrom shorewith fishgill microbialbiomassand alphadiversity. Subset and f distancefromshorewithalpha diversitymetrics (Chao1).g Comparison of fish of fish from EPO and Atlantic (n = 54) collected from ocean (excludes bay and mass and h distance from shore with alpha diversity metric: Faith's PD. estuary samples) and from the neritic zone (<200 m depth). a Correlation matrix c–h (Confidenceintervalsof 95% aredisplayedasdotted lines). habita- between sample metadata where values are rho and significance indicated by t_act_collection refers to the metadata column name from where this habitat clas- *p <0.05, **p <0.01, ***p <0.001, ****p <0.0001 (Spearmancorrelation). sification can be found…, SZsurf zone, RIT rocky intertidal, RST rocky subtidal, IS b Comparison of gill microbial biomass (log cells per gram) across habitat types inner shelf, KBRF kelp bed rocky reef, MDRF mid depth rocky reef, CP coastal from which the fish were collected. Distribution is in median and interquartile pelagic, P pelagic. Mass_g_log = log 10 (mass of the fish in grams), dis- range. Statistical differences determined using non-parametric testing tance_from_shore_m_log = log 10 (distance from nearest point on shore in meters Kruskal–Wallistest with 0.05 FDRBenjamini–Hochberg. c Comparison of fish mass from where the fish was caught).
Fig. 2 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 2 | Limit of detection, sample exclusion, and microbial biomassestimation for FMP101 dataset. a Application of KatharoSeq formula to calculate limit of detection of microbiota platesusing the Bacillus/Paracoccus mock community (1150 reads at 90%). b Limit of detection based on cell counts of Bacillus/ Paracoccus mock community (~16 cells into extraction at 90%). c Model fit of the log(sequencing read counts) of positive extraction controls vs. the log cell counts of those positive extraction controls (empirically determined using plate counts. The linear regression of the line is indicative of the quality of method to estimate microbial biomass from sequencing read counts. Con- fidenceintervalsof 95% aredisplayedas dottedlines. Thismethodissimilar to a qPCR curvewherethe log (Ct) would beequivalent to the log(read counts).This equation is then used to estimate the number of "microbial density" of the existing samples which is then further normalized by the volume of the DNA extraction,biomass of materialgoinginto theextraction and finallynormalized to at estimated microbial cells per gram of tissue. d Community analysis comparison and validation of compositionality of controls of twosets of mock community controls (section 1 = Bacillus/Paracoccus mock community; section 2 = zymo mock community). Putative contaminant g__Geobacillus identified (presentin 93% of negatives and higherrelative abundance ascompared to positives and samples). e Number of samples successful across the four body sites collected from the broad fish microbiota dataset. QC quality control, g__ refers to a genus of bacteria, HM mock homemade mock or human made mixture of bacteria to use as a control whereas zymo mock = mock microbial community created by a company "Zymo".
Fig. 1 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 1 | Samplingdesignof 116 speciesof marine fish. a Using ArcGIS todepictthe general area from which fish were sampled: black dots indicate the locations of the 101 unique species of marine fish sampled from the California Current Ecosystem in the Eastern Pacific Ocean primarily in the waters of San Diego CA. Red circles depict the locations of an additional 17 species of fish (15 unique species with 2 species duplicates) collected from the Western Atlantic primarily in the waters of New York. When multiple species of fish were caught in the same location, a single circle is used to indicate the location. b Fish were sampled across a gradient of depth and distances from shore. c Biometric measurements taken for nearly all fish include total length, fork length, mass, gape, and GI length. Various ratios from these lengths were also calculated. Microbiota samples from the gill were primarily whole tissue specimens from the entire left second gill arch or a section of the top middle and bottom of the entire filament. Skin mucus samples were taken by scraping using a razor blade. Midgut digesta material was collected from directly posterior of the stomach or if stomach was absent, the beginning of the GI tract. Hindgut digesta samples were taken from near the anus. Image from phylopic. MG midgut, HG hindgut, GI gastrointestinal tract, m meters.
Microbial Biomass Dynamics at the Kellogg Biological Station, Hickory Corners, MI (1989 to 1996)
Dataset Abstract The purpose of these studies is to determine patterns of microbial biomass dynamics in crop, successional and forest communities. This project consists of two studies involving microbial biomass determination in soil samples taken from the Main Cropping System Experiment (KBS004): Chloroform Fumigation-Incubation; Soil Microbial Biomass C and N, and Epifluorescence Microscopy and Image Analysis; Soil Microbial Biovolume and C. The data for both studies are generated from subsets of the same soil samples, collected from treatments 1 – 8, CF, DF, and SF. See the descriptor (KBS004) for treatment descriptions. Samples are collected approximately monthly (during ice-free conditions) or annually (varies by year) from 5 sampling stations in each of 6 replicate plots per treatment; exceptions are treatment 8 (four reps), and treatments CF, DF, and SF (three reps each). For experiment descriptions, protocols, and data, see individual datasets. Samples were collected by K. Klingensmith in 1989 and by S. Halstead in other years. D. Harris performed lab analysis, data entry, data analysis, and data submission. Original data files not available; contact the LTER data manager with questions. The data currently available was derived from web-based files which were last reviewed by D. Harris on 10-01-1997 and may be available from archives. original data source http://lter.kbs.msu.edu/datasets/14
Data from: Environmental change alters nitrogen fixation rates and microbial parameters in a subarctic biological crust
<p>Together, Biological Soil Crust (BSC) and other cryptogamic groundcovers can contribute up to half of the global nitrogen (N) fixation. BSC also stabilizes the soil (reducing erosion and dust emissions), fixes carbon (C), retains moisture, and acts as a hotspot of microbial diversity and activity. Much of the knowledge about how climate change is affecting the composition and functioning of BSC comes from hot arid and semiarid regions. The comparatively smaller body of research on BSC from cold and mesic environments has been primarily observational, for example along chronosequences after a glacier retreat. Few studies have experimentally investigated the effects of the environment on BSC from high latitudes. Such experiments allow unraveling of relationships at a resolution that can only be achieved by controlling for confounding factors. We measured short-term (2-4 days) responses of a liverwort-based (Anthelia juratzkana) BSC from the south of Iceland to a range of temperature, moisture and light conditions. Warming increased N fixation rates, especially when moisture was at a saturation level, and only when light was not limiting. A correlation analysis suggests that increases in N fixation rates were linked to cyanobacterial abundance on the BSC surface and to the rates of their metabolic activity. Warming and moisture changes also induced compositional and structural modification of the bacterial community, with consequences at the functional level. In contrast to many observations on BSC from hot drylands, the BSC from our cold and mesic study site is more limited by low temperature and light than by moisture. Our findings show possible ways in which BSC from cold and mesic ecosystems can respond to short-term manifestations of climate change, such as increasingly frequent heat waves.</p>
SSU rRNA Gene Analysis of the Environmental and Biological Influences on Carbonate Precipitation within Hot Spring Microbial Mats in Little Hot Creek, CA
<p>R Markdown notebook and all data required to recreate the 16S rRNA Gene analysis figures found in Wilmeth et. al (In Submission), including mapping files, BIOM files, and R markdown notebook. </p>
Data from: Environmental change alters nitrogen fixation rates and microbial parameters in a subarctic biological crust
Open the record for dataset details and reuse information.
Soil Microbial Gene Sequences at the Kellogg Biological Station, Hickory Corners, MI (2004 to 2010)
Dataset Abstract Gene sequences extracted from soils. original data source http://lter.kbs.msu.edu/datasets/108
Multiple diversity facets of crucial microbial groups in biological soil crusts promote soil multifunctionality
<p>Aim: Microbial diversity is one of the most important factors for maintaining the performance of multiple functions in soils (multifunctionality). However, existing studies typically consider taxonomic richness or Shannon diversity of the entire community. We know little about the connection network of taxonomic and phylogenetic diversity facets and their collective impact on multifunctionality. In this study, the linkages of diversity effects within functional groups were disentangled in drylands.</p> <p>Location: The central Tibetan Plateau.</p> <p>Time period: Present.</p> <p>Major taxa studied: Phototrophs and diazotrophs (mainly cyanobacteria).</p> <p>Methods: Given the carbon- and nitrogen-limited nature of drylands, we conducted a high throughput sequencing of C/N-fixing functional groups from biocrusts and evaluated multiple diversity facets (i.e., richness, evenness, and phylogeny-related trait dissimilarity), together with seven crucial variables of soil functioning to calculate multifunctionality. The relations between multifaceted diversity and abundance with individual functions and multifunctionality were validated by a set of solid statistical analyses.</p> <p>Results: We found that the integrated biodiversity index was a stronger predictor of multifunctionality than richness. The divergent performance of different diversity facets determined the idiosyncratic effect of each functional group on soil multifunctionality. Moreover, the evaluation of functional significance at the species level gave important clues on the trade-offs and redundancy in each functional group, explaining the distinct patterns of diversity effects. Namely, richness was the dominant factor for diazotrophs to maximize multifunctionality, whereas phylogenetic dissimilarity was the essential one for phototrophs.</p> <p>Main conclusions: our study demonstrated that multiple diversity facets should be considered when grasping the biodiversity effects. In contrast with the community level, within-functional group measures may adequately capture the features of diversity that are most correlated with soil multifunctionality. Our results provided a perspective to bridge the gap between taxonomic and trait-based approaches for elucidating the biodiversity-ecosystem function relationship.</p>
Code and data sets analysed in: "Marine heatwave bleaching causes mass mortality and drives a microbial community reorganisation in an ecologically important temperate sponge" Bell et al. (2024). Global Change Biology
<p>The attached zipped folder contains in-situ, satellite, reanalysis and laboratory measurements, together with R and MATLAB scripts, to reproduce the results in Bell et al. (2024). Marine heatwave bleaching causes mass mortality and drives a microbial community reorganisation in an ecologically important temperate sponge. Global Change Biology. Each folder contains a read me file that describes the enclosed data sets and scripts.</p>
A Systems Biology Approach for Identification of Host and Microbial Mechanisms and Druggable Targets for the Treatment of PSC-IBD
ClinicalTrials.gov study NCT05376228. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Multiple diversity facets of crucial microbial groups in biological soil crusts promote soil multifunctionality
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