Spatial variations of bacterial community composition in sediments of the Jiaozhou Bay, China

Spatial variations of sediment microbes pose a great challenge for the estimation of anthropogenic influence on biogeochemical processes, yet remain very unclear in coastal ecosystems. Surface sediments in 9 stations from the eutrophic Jiaozhou Bay, China, were sampled, DNA was extracted within the sediments, and the 16S rDNA was sequenced with the Illumina Hiseq sequencing. Results reveal considerable heterogeneity of sediment bacteria in the Jiaozhou Bay, of which Proteobacteria and Bacteroidetes accounted for over 75%. Bacterial alpha-diversity indices decreased generally from the outside to the inner part of the bay and from the offshore to the nearshore area. Bacterial community structures of S3, S4, S7, and S8 clustered, those of S5, S13, and S14 grouped together, while those of S6 and S10 were distinct from each other and from those of the other stations. Major class Gammaproteobacteria were more abundant at the stations with mesoeutrophic to eutrophic levels (S4, S5, S8, and S10) and less abundant at oligotrophic stations (S6, S13, and S14), while Deltaproteobacteria had an opposite distribution pattern. Overall, bacterial community composition transitioned from being Xanthomonadales-dominant at S4 and S8 to being unidentifed_Gammaproteobacteria-dominant at S5, S6, S13, and S14, while in other stations there were comparable orders. The biogeochemical processes correspondingly changed from being nitrogen cycling-dominant at S4 and S8 to being sulfur cycling-dominant at S5, S6, S13, and S14. The bacterial distribution patterns were especially affected by the factors (dissolved organic phosphorus, DOP) in the overlying seawater due to the habitat status of P-insufficiency in the bay. Both orders Xanthomonadales and Alteromonadales could serve as bioindicators of anthropogenic pollution to different pollution types. At last, divergent distribution patterns of individual bacterial populations in the bay were revealed, the influential environmental gradients were clarified, and the uncertainty of microbes was reduced, helping to predict environmental functions in coastal areas.