Subsurface Bacterioplankton Structure and Diversity in the Strongly-Stratified Water Columns within the Equatorial Eastern Indian Ocean

Abstract

The consequences of climate change may directly or indirectly impact the marine biosphere. Although ocean stratification has been recognized as one of the crucial consequences of ocean warming, its impacts on several critical aspects of marine microbes remain largely unknown in the Indian Ocean. Here, we investigate the effects of water stratification, in both surface and subsurface layers, on hydrogeographic parameters and bacterioplankton diversity within the equatorial eastern Indian Ocean (EIO). Strong stratification in the upper 200 m of equatorial EIO was detected with evidential low primary productivity. The vertical bacterioplankton diversity of the whole water columns displayed noticeable variation, with lower diversity occurring in the surface layer than in the subsurface layers. Horizontal heterogeneity of bacterioplankton communities was also in the well-mixed layer among different stations. SAR11 and Prochlorococcus displayed uncharacteristic low abundance in the surface water. Some amplicon sequence variants (ASVs) were identified as potential biomarkers for their specific depths in strongly-stratified water columns. Thus, barriers resulting from stratification are proposed to function as an 'ASV filter' to regulate the vertical bacterioplankton community diversity along the water columns. Overall, our results suggest that the effects of stratification on the structure and diversity of bacterioplankton can extend up to the bathypelagic zone in the strongly-stratified waters of the equatorial EIO. This study provides the first insight into the effect of stratification on the subsurface microbial communities in the equatorial eastern Indian Ocean.

Keywords: bacteria; barrier layer; composition; eastern Indian Ocean; richness; stratification; temperature.

Figure 1

Map of the sampling stations on the equatorial eastern Indian Ocean. Three stations (I302, I309, and I313) were near the equator, and three stations (I407, I407, and I413) were on the equator. Sampling stations were plotted using the Ocean Data View software (https://odv.awi.de, accessed on 3 June 2020).

Figure 2

Depth profiles of environmental parameters. (a) Temperature (°C), (b) Salinity (PSU), (c) Density (sigma-t), (d) Brunt–Väisälä frequency (N²). Depth profiles of Chlorophyll and DO in the (e,f) entire water column and (g,h) upper 200 m water column. The region between dashed gray lines represents the deep chlorophyll maximum (DCM) layer and the oxycline layer.

Figure 3

The barrier layer thickness of the equatorial eastern Indian Ocean. The blue line represents the Isothermal Layer Depth (ILD), determined by the definition of the depth where the temperature was 0.5 °C below the surface temperature. The red line represents the Mixed Layer Depth (MLD), defined as the depth surface density plus the density difference brought about by the temperature increment of 0.2 °C. The shaded area represents the barrier layer thickness determined by the difference between the ILD and MLD (D_T-02-D_s).

Figure 4

Principal component analysis of measured environmental parameters. Each colored dot represents a sample collected at a specific depth. PC1 and PC2 explained 40.63% and 19.62% of the total variation, respectively.

Figure 5

Depth profiles of bacterial alpha diversity indices. (a) Shannon entropy, (b) Observed features (richness), and (c) Pielou’s evenness. The box in the plot represents the interquartile range (IQR), which ranges from the first to third quartiles. The line within the box represents the median. The whiskers extend from the box to the smallest and largest values within 1.5 times the IQR. Any values beyond the whiskers are plotted as individual data points, which may be considered outliers.

Figure 6

Nonmetric multidimensional scaling (NMDS) plot illustrating the dissimilarities among the bacterial communities of different depths.

Figure 7

Bar plot showing bacterial community compositions (at phylum level) of different sampling stations.

Figure 8

ANCOM generated volcano plots. The W value indicated the number of sub-hypotheses that have passed for a given taxa. Taxa with higher W were more significantly abundant in specific group versus another group. The clr value referred to the effect size change between the compared groups. Genera identified as statistically significant biomarkers were labelled. Positive clr indicated higher abundance in surface layer group against to subsurface layer group.

Figure 9

Heatmap of top 100 abundance genus. The relative abundance was log10 transformed and scaled by row. Genera (rows) are clustered by similarity using Ward’s hierarchical agglomerative method and labeled with results of ANCOM. Genera identified as biomarker of depths (p < 0.05) were labeled red in the bar of “Biomarker-depth”. Genera identified as biomarker in stratified-surface layer were labeled red, whereas genera identified as biomarker in subsurface layer were labeled blue in the bar of “Biomarker-layer” (p < 0.05).