Characterization of pollen and bacterial community composition in brood provisions of a small carpenter bee

2016 ◽  
Vol 25 (10) ◽  
pp. 2302-2311 ◽  
Author(s):  
Quinn S. McFrederick ◽  
Sandra M. Rehan
Keyword(s):  
Bacterial Community ◽  
Community Composition ◽  
Bacterial Community Composition ◽  
Carpenter Bee ◽  
Small Carpenter Bee

scholarly journals Collection of Environmental Variables and Bacterial Community Compositions in Marian Cove, Antarctica, during Summer 2018

Data ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 27
Author(s):  
Hyo-Ryeon Kim ◽  
Jae-Hyun Lim ◽  
Ju-Hyoung Kim ◽  
Il-Nam Kim
Keyword(s):  
Bacterial Community ◽  
Community Composition ◽  
Marine Bacteria ◽  
Environmental Variables ◽  
Environmental Changes ◽  
Bacterial Community Composition ◽  
Composition Data ◽  
Decomposition Of Organic Matter ◽  
Class Level ◽  
Earth’S Climate

Marine bacteria, which are known as key drivers for marine biogeochemical cycles and Earth’s climate system, are mainly responsible for the decomposition of organic matter and production of climate-relevant gases (i.e., CO₂, N₂O, and CH₄). However, research is still required to fully understand the correlation between environmental variables and bacteria community composition. Marine bacteria living in the Marian Cove, where the inflow of freshwater has been rapidly increasing due to substantial glacial retreat, must be undergoing significant environmental changes. During the summer of 2018, we conducted a hydrographic survey to collect environmental variables and bacterial community composition data at three different layers (i.e., the seawater surface, middle, and bottom layers) from 15 stations. Of all the bacterial data, 17 different phylum level bacteria and 21 different class level bacteria were found and Proteobacteria occupy 50.3% at phylum level following Bacteroidetes. Gammaproteobacteria and Alphaproteobacteria, which belong to Proteobacteria, are the highest proportion at the class level. Gammaproteobacteria showed the highest relative abundance in all three seawater layers. The collection of environmental variables and bacterial composition data contributes to improving our understanding of the significant relationships between marine Antarctic regions and marine bacteria that lives in the Antarctic.

scholarly journals Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danijela Šantić ◽  
Kasia Piwosz ◽  
Frano Matić ◽  
Ana Vrdoljak Tomaš ◽  
Jasna Arapov ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Bacterial Community ◽  
Microbial Diversity ◽  
Community Composition ◽  
Adriatic Sea ◽  
Bacterial Community Composition ◽  
Environmental Data ◽  
Large Contribution ◽  
Neural Network Analysis

AbstractBacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.

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