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Seasonal Dynamics of Marine Microbial Community in the South Sea of Korea.

Suh SS, Park M, Hwang J, Kil EJ, Jung SW, Lee S, Lee TK - PLoS ONE (2015)

Bottom Line: In addition, Psedoalteromonadaceae, Vibrionaceae and SAR11-1 were predominant members of the OTUs found in all sampling seasons.Environmental factors significantly influenced the bacterial community structure among season, with the phosphate and nitrate concentrations contributing strongly to the spatial distribution of the Alphaproteobacteria; the Gammaproteobacteria, Flavobacteria, and Actinobacteria all showed marked negative correlations with all measured nutrients, particularly silicon dioxide and chlorophyll-a.The results suggest that seasonal changes in environmental variables contribute to the dynamic structure of the bacterial community in the study area.

View Article: PubMed Central - PubMed

Affiliation: South Sea Environment Research Department, Korea Institute of Ocean Science and Technology, Geoje, 656-830, Republic of Korea.

ABSTRACT
High-resolution 16S rRNA tag pyrosequencing was used to obtain seasonal snapshots of the bacterial diversity and community structure at two locations in Gosung Bay (South Sea, Korea) over a one year period. Seasonal sampling from the water column at each site revealed highly diverse bacterial communities containing up to 900 estimated Operational Taxonomic Units (OTUs). The Alphaproteobacteria and Gammaproteobacteria were the most abundant groups, and the most frequently recorded OTUs were members of Pelagibacter and Glaciecola. In particular, it was observed that Arcobacter, a genus of the Epsilonproteobacteria, dominated during summer. In addition, Psedoalteromonadaceae, Vibrionaceae and SAR11-1 were predominant members of the OTUs found in all sampling seasons. Environmental factors significantly influenced the bacterial community structure among season, with the phosphate and nitrate concentrations contributing strongly to the spatial distribution of the Alphaproteobacteria; the Gammaproteobacteria, Flavobacteria, and Actinobacteria all showed marked negative correlations with all measured nutrients, particularly silicon dioxide and chlorophyll-a. The results suggest that seasonal changes in environmental variables contribute to the dynamic structure of the bacterial community in the study area.

No MeSH data available.


UniFrac distance-based Jackknife clustering of bacterial communities associated with different seasonal water masses from different sampling locations.Unifrac PCoA images were captured from 3D UniFrac PCoA to illustrate differences in the microbiota among the different samples. The following UniFrac PCoA analyses were based on the OTU data, with only the first three principal coordinates (PCs) shown: unweighted UniFrac with PC1 = 44.180, PC2 = 21.744, and PC3 = 13.6003% (p = 0.001).
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pone.0131633.g005: UniFrac distance-based Jackknife clustering of bacterial communities associated with different seasonal water masses from different sampling locations.Unifrac PCoA images were captured from 3D UniFrac PCoA to illustrate differences in the microbiota among the different samples. The following UniFrac PCoA analyses were based on the OTU data, with only the first three principal coordinates (PCs) shown: unweighted UniFrac with PC1 = 44.180, PC2 = 21.744, and PC3 = 13.6003% (p = 0.001).

Mentions: The constitution of 16S rRNA gene sequences among the seasonal samples was assessed using the weighted UniFrac clustering method. The statistical analysis showed that the bacterial communities in spring and winter water column samples clustered together. Interestingly, the microbial community associated with summer seawater was significantly different from the communities in other seasons, as indicated by the PCoA based on the unweighted UniFrac distance (Fig 5). Canonical correspondence analysis (CCA) results for several microbial assemblages in relation to several environmental factors are shown in Fig 6. In this figure the correlations between specific environmental factors and microbial groups are represented by the angle of the arrows between them. The data indicate that the temporal distribution of several major microbial assemblages was mainly influenced by the nutrient conditions and several other environmental factors. Phosphate and nitrate contributed substantially to the spatial distribution of the Alphaproteobacteria, while silicon dioxide was correlated with the occurrence of Epsilonproteobacteria and Chroobacteria (Fig 6A). In contrast, the occurrences of Gammaproteobacteria, Flavobacteria, and Actinobacteria were negatively correlated with all of the nutrients noted above, particularly silicon dioxide and chlorophyll-a. With regard to correlations with environmental factors (Fig 6B), salinity showed a marked correlation with the spatial distribution of Flavobacteria and Actinobacteria, the Alphaproteobacteria were positively correlated with dissolved oxygen (DO), and the abundance of Epsilonproteobacteria and Chroobacteria negatively correlated with the levels of all environmental factors, particularly salinity.


Seasonal Dynamics of Marine Microbial Community in the South Sea of Korea.

Suh SS, Park M, Hwang J, Kil EJ, Jung SW, Lee S, Lee TK - PLoS ONE (2015)

UniFrac distance-based Jackknife clustering of bacterial communities associated with different seasonal water masses from different sampling locations.Unifrac PCoA images were captured from 3D UniFrac PCoA to illustrate differences in the microbiota among the different samples. The following UniFrac PCoA analyses were based on the OTU data, with only the first three principal coordinates (PCs) shown: unweighted UniFrac with PC1 = 44.180, PC2 = 21.744, and PC3 = 13.6003% (p = 0.001).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4487691&req=5

pone.0131633.g005: UniFrac distance-based Jackknife clustering of bacterial communities associated with different seasonal water masses from different sampling locations.Unifrac PCoA images were captured from 3D UniFrac PCoA to illustrate differences in the microbiota among the different samples. The following UniFrac PCoA analyses were based on the OTU data, with only the first three principal coordinates (PCs) shown: unweighted UniFrac with PC1 = 44.180, PC2 = 21.744, and PC3 = 13.6003% (p = 0.001).
Mentions: The constitution of 16S rRNA gene sequences among the seasonal samples was assessed using the weighted UniFrac clustering method. The statistical analysis showed that the bacterial communities in spring and winter water column samples clustered together. Interestingly, the microbial community associated with summer seawater was significantly different from the communities in other seasons, as indicated by the PCoA based on the unweighted UniFrac distance (Fig 5). Canonical correspondence analysis (CCA) results for several microbial assemblages in relation to several environmental factors are shown in Fig 6. In this figure the correlations between specific environmental factors and microbial groups are represented by the angle of the arrows between them. The data indicate that the temporal distribution of several major microbial assemblages was mainly influenced by the nutrient conditions and several other environmental factors. Phosphate and nitrate contributed substantially to the spatial distribution of the Alphaproteobacteria, while silicon dioxide was correlated with the occurrence of Epsilonproteobacteria and Chroobacteria (Fig 6A). In contrast, the occurrences of Gammaproteobacteria, Flavobacteria, and Actinobacteria were negatively correlated with all of the nutrients noted above, particularly silicon dioxide and chlorophyll-a. With regard to correlations with environmental factors (Fig 6B), salinity showed a marked correlation with the spatial distribution of Flavobacteria and Actinobacteria, the Alphaproteobacteria were positively correlated with dissolved oxygen (DO), and the abundance of Epsilonproteobacteria and Chroobacteria negatively correlated with the levels of all environmental factors, particularly salinity.

Bottom Line: In addition, Psedoalteromonadaceae, Vibrionaceae and SAR11-1 were predominant members of the OTUs found in all sampling seasons.Environmental factors significantly influenced the bacterial community structure among season, with the phosphate and nitrate concentrations contributing strongly to the spatial distribution of the Alphaproteobacteria; the Gammaproteobacteria, Flavobacteria, and Actinobacteria all showed marked negative correlations with all measured nutrients, particularly silicon dioxide and chlorophyll-a.The results suggest that seasonal changes in environmental variables contribute to the dynamic structure of the bacterial community in the study area.

View Article: PubMed Central - PubMed

Affiliation: South Sea Environment Research Department, Korea Institute of Ocean Science and Technology, Geoje, 656-830, Republic of Korea.

ABSTRACT
High-resolution 16S rRNA tag pyrosequencing was used to obtain seasonal snapshots of the bacterial diversity and community structure at two locations in Gosung Bay (South Sea, Korea) over a one year period. Seasonal sampling from the water column at each site revealed highly diverse bacterial communities containing up to 900 estimated Operational Taxonomic Units (OTUs). The Alphaproteobacteria and Gammaproteobacteria were the most abundant groups, and the most frequently recorded OTUs were members of Pelagibacter and Glaciecola. In particular, it was observed that Arcobacter, a genus of the Epsilonproteobacteria, dominated during summer. In addition, Psedoalteromonadaceae, Vibrionaceae and SAR11-1 were predominant members of the OTUs found in all sampling seasons. Environmental factors significantly influenced the bacterial community structure among season, with the phosphate and nitrate concentrations contributing strongly to the spatial distribution of the Alphaproteobacteria; the Gammaproteobacteria, Flavobacteria, and Actinobacteria all showed marked negative correlations with all measured nutrients, particularly silicon dioxide and chlorophyll-a. The results suggest that seasonal changes in environmental variables contribute to the dynamic structure of the bacterial community in the study area.

No MeSH data available.