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287 results for “Bacterial communities”
Tree-Associated Fungal and Bacterial Communities at Harvard Forest 2021
Cities are investing in tree-planting initiatives to protect their citizens from climate change-related heat and pollution exposure, yet Boston’s street trees are growing nearly four times as fast and dying twice as young as Massachusetts’ rural forest trees. Our research aims to characterize the belowground variables and microbial community composition that might explain the differences in growth and mortality rates observed between urban and rural trees. In 2021, soil, leaf, and root samples were taken from 25 trees in Harvard Forest to use as a rural comparison to Boston’s street trees and trees in other forests along an urban-to-rural gradient from Boston into Western Massachusetts. At each tree, three 12” deep, 2.4-centimeter radius soil cores were taken within the drip line, and soil cores were divided into the top 6” and lower 6” of soil. Fine roots were picked from each soil core. Six leaf samples were taken from the mid-canopy of each tree, where possible. Soil variables including temperature, moisture, percent organic matter, soluble nitrogen availability, bulk density, and root biomass were measured. Thus far, we have found that urban trees have fewer roots than Harvard Forest trees (F1,252) = 10.88, p = 0.0011), and that urban trees establish more root biomass deeper into the soil than Harvard Forest trees (p = 4.84e-5).
Microbial Observatory at North Temperate Lakes LTER Time series of bacterial community dynamics in Lake Mendota 2000 - 2009
With an unprecedented decade-long time series from a temperate eutrophic lake, we analyzed bacterial and environmental co-occurrence networks to gain insight into seasonal dynamics at the community level. We found that (1) bacterial co-occurrence networks were non-random, (2) season explained the network complexity and (3) co-occurrence network complexity was negatively correlated with the underlying community diversity across different seasons. Network complexity was not related to the variance of associated environmental factors. Temperature and productivity may drive changes in diversity across seasons in temperate aquatic systems, much as they control diversity across latitude. While the implications of bacterioplankton network structure on ecosystem function are still largely unknown, network analysis, in conjunction with traditional multivariate techniques, continues to increase our understanding of bacterioplankton temporal dynamics.
Data from: Shift of bacterial and fungal communities upon soil amelioration is driven by carbon degradability of organic amendments
<p>Microbial communities of bacteria and fungi have been analyzed in soil. Agricultural soil was amended with different organic amendments including straw, compost, biogas residues, and biochar, and incubated in the lab. After 6 months, DNA extracted from soil samples was analyzed via Illumia MiSeq DNA sequencing (16S V3V4 for bacteria, ITS1 for fungi) to evaluate changes to the microbial community structure.</p> <p>For details, please see the respective publication (DOI: 10.1007/s44378-024-00012-5).</p>
Morpho-anatomical traits explain the effects of bacterial-feeding nematodes on soil bacterial community composition and plant growth and nutrition
<p>Soil Bacterial populations</p> <p>V3-V4, of the 16S rRNA gene using the primers 341F CCTAYGGGRBGCASCAG and 806R GGACTACNNGGGTATCTAAT.</p>
Catalog of GenBank sequence read archive (SRA) entries of 16S and 18S rRNA genes from bacterial and protistan planktonic communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2011-2013
Microbial communities in the coastal Arctic Ocean experience extreme variability in organic matter and inorganic nutrients driven by seasonal shifts in sea ice extent and freshwater inputs. Lagoons border more than half of the Beaufort Sea coast and provide important habitats for migratory fish and seabirds; yet, little is known about the planktonic food webs supporting these higher trophic levels. To investigate seasonal changes in bacterial and protistan planktonic communities, amplicon sequences of 16S and 18S rRNA genes were generated from samples collected during periods of ice-cover (April), ice break-up (June), and open water (August) from shallow lagoons along the eastern Alaska Beaufort Sea coast from 2011 through 2013. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA530074 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA530074. This data package is associated with the following publication: Kellogg CTE, McClelland JW, Dunton KH and Crump BC (2019) Strong Seasonality in Arctic Estuarine Microbial Food Webs. Front. Microbiol. 10:2628. doi: 10.3389/fmicb.2019.02628 Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provided site codes (column "site_name" here) and collection dates (column "collection_date" here) in each dataset. Note that the site codes in this package are without hyphens (e.g. JAA) while site codes in the above environmental data package have hyphens (e.g. JA-A). Instead of citing this package which is jus
Catalog of GenBank sequence read archive (SRA) entries of metagenomic DNA sequence analyses of bacterial and archaeal water column communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2012
In contrast to temperate systems, Arctic lagoons that span the Alaska Beaufort Sea coast face extreme seasonality. Nine months of ice cover up to ∼1.7 m thick is followed by a spring thaw that introduces an enormous pulse of freshwater, nutrients, and organic matter into these lagoons over a relatively brief 2–3 week period. Prokaryotic communities link these subsidies to lagoon food webs through nutrient uptake, heterotrophic production, and other biogeochemical processes, but little is known about how the genomic capabilities of these communities respond to seasonal variability. This study characterizes the metabolic capabilities of microbial communities across three seasons in two lagoons and one open coastal site along the eastern Alaska Beaufort Sea coast. We used metagenomic DNA sequence data of bacterial and archaeal water column communities to identify genes of relevant biogeochemical pathways. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA642637 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA642637. This data package is associated with the following publication: Baker, Kristina D., Colleen T. E. Kellogg, James W. McClelland, Kenneth H. Dunton, and Byron C. Crump. “The Genomic Capabilities of Microbial Communities Track Seasonal Variation in Environmental Conditions of Arctic Lagoons.” Frontiers in Microbiology 12 (2021). https://doi.org/10.3389/fmicb.2021.601901. Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provi
SMB01 Variation in soil respiration and bacterial community due to species-specific plant-soil history at konza prairie
We conducted a “home vs. away” plant-soil feedback greenhouse experiment using two C3 grass species (Bromus inermis and Pascopyrum smithii) grown in soil collected from Konza Prairie. We used a closed-circuit CO2 trapping method and isotopic analysis to differentiate between root-derived and SOM-derived CO2 production. We investigated how soil chemistry and soil bacterial communities differed in soils with a history of B. inermis vs soils with a history of P. smithii.
EMA-amplicon-based taxonomic characterisation of the viable bacterial community present in untreated and SODIS treated roof-harvested rainwater
<p>Dataset for publication: EMA-amplicon-based taxonomic characterisation of the viable bacterial community present in untreated and SODIS treated roof-harvested rainwater, Strauss et al. (2018). DOI: 10.1039/c8ew00613j.</p>
Metagenomic and metaproteomic insights into bacterial communities in leaf-cutter ant fungus gardens
<p>The submitted protein sequences were compiled from two of our previous studies, 1) 'Metagenomic and metaproteomic insights into bacterial communities in leaf-cutter ant fungus gardens' (doi.org/10.1038/ismej.2012.10) and 2) 'Leucoagaricus gongylophorus Produces Diverse Enzymes for the Degradation of Recalcitrant Plant Polymers in Leaf-Cutter Ant Fungus Gardens' (doi.org/10.1128/AEM.03833-12).</p>
Baltimore Ecosystem Study: Stream biofilm bacterial community composition
The Baltimore Ecosystem Study stream biofilm bacterial community composition was obtained from 8 long-term sampling network sites in and near the Gwynns Falls watershed to examine how bacterial communities differ along an urban-rural gradient. Sampling was conducted at the same time as stream chemistry sampling on 18 June 2014 and 21 Oct 2014. Note: biofilm samples were taken about 50 meters east from the Carroll Park monitoring station, just under the I95 highway overpass, due to high water depth, high water flow, and lack of rock substrates for sampling. This dataset presents the number of sequences matching the taxonomic classifications in a reference database of 16S rRNA genes. See the full metadata record for detailed methods.
Bacterial and archaeal communities in the abyssal Clarion-Clipperton Zone: spatial synthesis results
<p>Datasets associated with a manuscript synthesizing previously published results and a new dataset on bacterial and archaeal community composition from the abyssal Clarion-Clipperton Zone, including communities from deep seawater, sediments, and polymetallic nodules.</p>
Data from: Fungal communities are important determinants of bacterial community composition in deadwood
<p>Fungal-bacterial interactions play a key role in the functioning of many ecosystems. Thus, understanding their interactive dynamics is of central importance for gaining predictive knowledge on ecosystem functioning. However, it is challenging to disentangle the mechanisms behind species associations from observed co-occurrence patterns and little is known about the directionality of such interactions. Here we apply joint species distribution modelling to high-throughput sequencing data on co-occurring fungal and bacterial communities in deadwood to ask whether fungal and bacterial co-occurrences result from shared habitat use (i.e. dead wood's properties), or whether there are fungal-bacterial interactive associations after habitat characteristics are taken into account. Moreover, we test the hypothesis that the interactions are mainly modulated through fungal communities influencing bacterial communities. For that, we quantified how much the predictive power of the joint species distribution models for bacterial and fungal community improved when accounting for the other community. Our results show that fungi and bacteria form tight association networks (i.e. some species pairs co-occur more frequently and other species pairs co-occur less frequently than expected by chance) in deadwood that include common (or opposite) responses to the environment, as well as (potentially) biotic interactions. Additionally, we show that information about the fungal occurrences and abundances increased the power to predict the bacterial abundances substantially, whereas information about the bacterial occurrences and abundances increased the power to predict the fungal abundances much less. Our results suggest that fungal communities may mainly affect bacteria in deadwood.</p> <p><b>Importance</b></p> <p>Understanding the interactive dynamics between fungal and bacterial communities is important to gain predictive knowledge on ecosystem functioning. However little is known about the mechanisms behind fungal-bacterial associations and the directionality of species interactions. Applying joint species distribution modelling to high throughput sequencing data on co-occurring fungal-bacterial communities in deadwood, we found evidence that non-random fungal-bacterial associations derive from shared habitat use, as well as (potentially) biotic interactions. Importantly,<i> </i>the combination of cross-validations and conditional cross-validations helped us to answer the question about the directionality of the biotic interactions, providing evidence that suggests that fungal communities may mainly affect bacteria in deadwood. Our modelling approach may help gaining insight into the directionality of interactions between different components of the microbiome in other environments.</p>
Siderophores drive invasion dynamics in bacterial communities through their dual role as public good versus public bad
<p>Microbial invasions can compromise ecosystem services and spur dysbiosis and disease in hosts. Nevertheless, the mechanisms determining invasion outcomes often remain unclear. Here, we examine the role of iron-scavenging siderophores in driving invasions of <em>Pseudomonas aeruginosa</em> into resident communities of environmental pseudomonads. Siderophores can be "public goods" by delivering iron to individuals possessing matching receptors; but they can also be "public bads" by withholding iron from competitors lacking these receptors. Accordingly, siderophores should either promote or impede invasion, depending on their effects on invader and resident growth. Using supernatant feeding and invasion assays, we show that invasion success indeed increased when the invader could use its siderophores to inhibit (public bad) rather than stimulate (public good) resident growth. Conversely, invasion success decreased the more the invader was inhibited by the residents' siderophores. Our findings identify siderophores as a major driver of invasion dynamics in bacterial communities under iron-limited conditions.</p>
Comparative analysis of surface sanitization protocols on the bacterial community structures in the hospital environment
<p>In this study, we used 16S rRNA gene sequencing approaches to characterize the bacterial microbiota on different surfaces of the hospital environment. The longitudinal data was then subjected to comprehensive comparisons between different sanitation strategies (disinfectants, detergents and probiotics) to measure their potential effect on the microbial community structures in the hospital environment.</p> <p>This archive contains results and data of the 16S rRNA amplicon sequencing performed on 1019 environmental and 271 patient DNA samples collected over the time course of 40 weeks in a newly opened ward in the neurological station at the Charité Hospital (Berlin). The files include a study information and sample metadata sheets, BIOM-tables and information about the taxonomy results and diversity metrics.</p>
Bacterial community composition of bulk soil from date palm (Phoenix dactylifera) farm depend on irrigation water salinity
<p>Non-saline and saline ground water irrigation is extensively used in the arid regions of United Arab Emirates (UAE) for date palm (<em>Phoenix</em> <em>dactylifera</em>) cultivation without knowing its effect on bulk soil bacterial communities. Bulk soil acts as a supply base for microbes and nutrients that are accessed by date palm roots. We collected soil samples from date farms across UAE and performed V3-V4 16s rRNA metabarcoding analysis to understand how bulk soil bacterial diversity and communities respond to irrigation water sources (non-saline and saline groundwater irrigation). There was no significant variation in bulk bacterial diversity (Shannon diversity, richness as well as evenness). But bulk bacterial communities differed between irrigation water sources and irrigation water electrical conductivity was the significant factor that explained a part of community variation. Out of total 5089 OTUs, saline bulk soil harbored only 21.3% of total OTUs compared to 31.5% OTUs in non-saline bulk soil, while 47.15% OTUs shared between both types of irrigation. Proteobacteria abundance was higher in saline bulk soil, while Actinobacteriota abundance was enhanced in non-saline bulk soil. Similar selection was observed at genus level, wherein saline bulk soil showed increase in abundance of <em>Subgroup_10, Nitrospira </em>and<em> Mycobacterium</em>, whereas <em>Microvirga, Ammoniphilus, Nitrospira</em> and <em>Lysinibacillus </em>were elevated in non-saline bulk soil. Saline (<em>Novibacillus</em> and <em>Bauldea</em>) and non-saline bulk soil (<em>Microvirga</em>, <em>Marmoricola</em>, <em>Domibacillus</em>, <em>Oceanobacillus</em>, <em>Bhargavaea</em> and <em>Solirubrobacter</em>) showed significant selection of indicator taxa (P < 0.05). This indicate that bacterial communities colonizing bulk soil differ depending on irrigation water source and it is affected by irrigation water EC.</p>
Community diversity determines the evolution of synthetic bacterial communities under artificial selection
<p>These data and script are related to the article entitled "Community diversity determines the evolution of synthetic bacterial communities under artificial selection". This study reports the results of an experiment that involved bacterial communities differing for their richness level (1, 2, 4, 8, 16 species) and subjected to artificial selection or not. The property under selection was the optical density (OD). All the data needed to reproduce the figures and tables presented in the manuscript are provided.</p>
OTU level 16s sequence data for "Algae drive convergent bacterial community assembly when nutrients are scarce"
<p>16s sequence data at the OTU level for the experiments conducted in "Algae drive convergent bacterial community assembly when nutrients are scarce"</p> <p>The file is in fasta format, which can be read by many software packages including biopython, R, and SILVA's alignment, classification and tree service.</p> <p>The sequence ids can be used to find the phylogeny and OTU ID of the sequences from Supplementary dataset 4. </p>
Biom files of bacterial rhizosphere communities from two pioneer species, Brachystegia boehmii and B. spiciformis
<p>In this repository, you will find a mapping file of all samples and a biom table with all operational taxonomy units allowing the analyses of the bacterial rhizosphere communities.</p>
Different facets of bacterial and fungal communities drive soil multifunctionality in grasslands spanning a 3,500 km transect
<p>1. Soil microbial communities are essential in regulating ecosystem functions and services. However, the importance of bacterial and fungal communities as predictors of multiple soil functions (i.e., soil multifunctionality) in grassland ecosystems has not been studied systematically.</p> <p>2. Here, we measured soil microbial diversity, community composition, biomass, and multiple soil functions of 41 sites in five grassland ecosystems spanning a 3,500 km northeast–southwest transect. The random forest algorithm was adopted to determine the importance of geographical location, climatic, altitude, edaphic, plant, and microbial predictors in driving a proxy of soil multifunctionality (seven soil functions in this study). Moreover, structural equation models (SEMs) were employed to examine the direct and indirect effects of those predictors on soil multifunctionality.</p> <p>3. Our results demonstrated that soil multifunctionality was positively driven by soil fungal diversity but not by bacterial diversity. Fungal phylogenetic diversity (presence of different evolutionary lineages) showed stronger positive relationships with soil multifunctionality than taxonomic diversity (richness of species). Dominant bacterial taxa, particularly of phyla Actinobacteria and Proteobacteria, were positively associated with soil multifunctionality, while none of the fungal taxa were found to regulate soil multifunctionality. Furthermore, both fungal and bacterial biomass had significant effects on soil multifunctionality, while the effect of microbial biomass was weaker than that of fungal diversity and bacterial taxa. Importantly, the direct positive effects of soil fungal diversity, dominant bacterial taxa, and fungal and bacterial biomass were maintained after accounting for multiple predictors in grassland ecosystems.</p> <p>4. This study provided strong empirical evidence that soil multifunctionality was driven by different facets of the bacterial and fungal communities in the grassland ecosystems. Our results also highlighted that any loss of fungal diversity, dominant bacterial taxa and microbial biomass might reduce soil multifunctionality, exacerbating ecosystem functions and services such as soil fertility, primary production, and climate mitigation in grassland ecosystems. </p>
Bacterial Community Weighted rrn Operon Copy Numbers and Enzyme Activity in Costa Rica and Alaska Soils.
<p>Datasets of bacterial community weighted rrn operon copy number and enzyme activity in Alaska and Costa Rica soils. The Alaska soils were collected from across four elevational terraces in a tidal wetland on the western coast. The Costa Rica soils were collected from plots subjected to precipitation manipulation treatments in La Selva Biological Research Station. </p> <p>The variable descriptions for the Alaska dataset are as follows: simple_id: an identifier variable. id: an identifier variable that includes terrace identity. transect_id: a variable that identifies collection transect location. BG_umol_g_h: the activity rate of beta-glucosiadase in micromoles per gram soil per hour. NAG_umol_g_h: the activity rate of N-acetyl-glucosaminidase in micromoles per gram soil per hour. LAP_umol_g_h: activity rate of leucine-aminopeptidase in micromoles per grams soil per hour. AP_umol_g_h: the activity rate of acid phosphatase in micromoles per gram soil per hour. BG_g_C: the activity rate of beta-glucosiadase in micromoles per gram soil carbon per hour. NAG-g_N: activity rate of N-acetyl-glucosaminidase in micromoles per gram soil nitrogen per hour. LAP_g_N: activity rate of leucine-aminopeptidase in micromoles per gram soil nitrogen per hour. AP_mg_P: activity rate of acid-phosphatase in micromoles per gram soil phosphorus per hour. P_mg_kg: soil phosphorus concentration in milligrams phosphorus per kilogram soil. K_mg_kg: soil potassium concentration in milligrams potassium per kilogram soil. C_pct: soil carbon concentration in percent. N_pct: soil nitrogen concentration in percent. pH: soil pH. cn_ratio: the carbon-to-nitrogen ratio of soil. cp_ratio: the carbon-to-phosphorus ratio of soil. np_ratio: the nitrogen to phosphorus ratio of soil. weighted_copy_number: the bacterial community weighted rrn operon copy number.</p> <p>The variable descriptions for the Costa Rica dataset are as follows: ID: a variable that identifies the soil core collected. Plot: a variable that identifies the experimental plot from which soils were collected. Precip: a variable that describes the precipitation manipulation treatment applied. Core: a variable that identifies whether soil cores were enclosed in mesh or not. Moisture: the soil moisture of the collected core in grams water per grams dry soil. weighted copy_number: the bacterial community weighted rrn operon copy number. AP: activity rate of acid-phosphatase in micromoles per gram soil per hour. BG: the activity rate of beta-glucosiadase in micromoles per gram soil per hour.</p> <p>Also included are files of the code used to analyze the data in the R Statistical Computing Environment.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.