Diversity, composition and potential roles of sedimentary microbial communities in different coastal substrates around subtropical Okinawa Island, Japan

Abstract

Background: Marine benthic prokaryotic communities play crucial roles in material recycling within coastal environments, including coral reefs. Coastal sedimentary microbiomes are particularly important as potential reservoirs of symbiotic, beneficial, and pathogenic bacteria in coral reef environments, and therefore presumably play a core role in local ecosystem functioning. However, there is a lack of studies comparing different environments with multiple sites on the island scale, particularly studies focusing on prokaryotic communities, as previous investigations have focused mainly on a single site or on specific environmental conditions. In our study, we collected coastal sediments from seven sites around Okinawa Island, Japan, including three different benthic types; sandy bottoms, seagrass meadows, and hard substratum with living scleractinian corals. We then used metabarcoding to identify prokaryotic compositions and estimate enzymes encoded by genes to infer their functions.

Results: The results showed that the three substrata had significantly different prokaryotic compositions. Seagrass meadow sites exhibited significantly higher prokaryotic alpha-diversity compared to sandy bottom sites. ANCOM analysis revealed that multiple bacterial orders were differentially abundant within each substratum. At coral reef sites, putative disease- and thermal stress-related opportunistic bacteria such as Rhodobacterales, Verrucomicrobiales, and Cytophagales were comparatively abundant, while seagrass meadow sites abundantly harbored Desulfobacterales, Steroidobacterales and Chromatiales, which are common bacterial orders in seagrass meadows. According to our gene-coded enzyme analyses the numbers of differentially abundant enzymes were highest in coral reef sites. Notably, superoxide dismutase, an important enzyme for anti-oxidative stress in coral tissue, was abundant at coral sites. Our results provide a list of prokaryotes to look into in each substrate, and further emphasize the importance of considering the microbiome, especially when focusing on environmental conservation.

Conclusion: Our findings prove that prokaryotic metabarcoding is capable of capturing compositional differences and the diversity of microbial communities in three different environments. Furthermore, several taxa were suggested to be differentially more abundant in specific environments, and gene-coded enzymic compositions also showed possible differences in ecological functions. Further study, in combination with field observations and temporal sampling, is key to achieving a better understanding of the interactions between the local microbiome and the surrounding benthic community.

Keywords: Bioindicator; Coral reefs; Prokaryotic community; Sandy bottoms; Seagrass meadows.

Fig. 1

Maps showing sites investigated in the present research. (A): Okinawa Islands in the Japanese Archipelago. (B): Okinawa Island in detail. Each dot represents a study site, and color indicates substratum. Blue = coral reef, red = sandy bottom, green = seagrass meadow sites, respectively

Fig. 2

Bar plot showing the top 13 phyla in each benthic type. Different colors show different bacterial phyla. Columns show samples from coral reefs, seagrass meadows, and sandy bottom sites, from left to right. Color bar widths indicate relative abundance of each taxon

Fig. 3

nMDS plot of Bray-Curtis dissimilarity index of samples. Each circle, triangle or square represents a different sample. Sites have been abbreviated as follows: Kn: Kin, Sn: Sunabe, Ky: Kayou, Ur: Uruma, Sk: Sesoko, Mn: Manza, Od: Odo. Different colored mesh indicates groupings based on silhouette analysis results, mostly corresponding to red = sandy bottoms, blue = coral reefs, green = seagrass meadows

Fig. 4

Box plots of prokaryotic (left) and enzymic (right) Shannon diversity indices among different types of substrata. Asterisks show statistical significance (*** p < 0.005)

Fig. 5

Combined graph of the results obtained with ANCOM analysis (left to middle) and the total absolute abundance (right) of each bacterial order. For the ANCOM analysis, compared substratum types are shown at the top of each column. “W_statistics” stands for a log fold change value divided by standard error for effect size correction, and indicates to what type of substratum each bacterial order was differentially abundant. Asterisks show statistical significances (*p < 0.05, ** p < 0.01, ***p < 0.001)

Fig. 6

Combined graph showing the results of correlation among bacterial orders. From the top to bottom, rows show coral reef sites, sandy bottom sites, and seagrass meadow sites, respectively. Left column is based on the distance method, while right column is based on Pearson’s coefficient of correlation method, where warm colors mean positive correlation, and cold colors mean negative correlation

Fig. 7

Combined graph of the results obtained with the ANCOM analysis (left to middle) and the total absolute abundance (right) of each coded enzyme by prokaryotes. For the ANCOM analysis, compared substratum types are shown at the top of each column. “W_statistics” stands for a log fold change value divided by standard error for effect size correction and indicates to what type of substratum each bacterial order was differentially abundant. Asterisks show statistical significance (*p < 0.05, ** p < 0.01, ***p < 0.001)