Effect of Water Chemistry, Land Use Patterns, and Geographic Distances on the Spatial Distribution of Bacterioplankton Communities in an Anthropogenically Disturbed Riverine Ecosystem

Abstract

The spatial distribution of bacterioplankton communities in rivers is driven by multiple environmental factors, including local and regional factors. Local environmental condition is associated with effect of river water chemistry (through species sorting); ecological process in region is associated with effects of land use and geography. Here, we investigated variation in bacterioplankton communities (free-living, between 0.22 and 5 μm) in an anthropogenically disturbed river using high-throughput DNA sequencing of community 16S rRNA genes in order to investigate the importance of water chemistry, land use patterns, and geographic distance. Among environmental factors, sulfate (SO₄ ^2-), manganese (Mn), and iron (Fe) concentrations were the water chemistry parameters that best explained bacterioplankton community variation. In addition, forest and freshwater areas were the land use patterns that best explained bacterioplankton community variation. Furthermore, cumulative dendritic distance was the geographic distance parameter that best explained bacterial community variation. Variation partitioning analysis revealed that water chemistry, land use patterns, and geographic distances strongly shaped bacterioplankton communities. In particular, the direct influence of land use was prominent, which alone contributed to the highest proportion of variation (26.2% in wet season communities and 36.5% in dry season communities). These results suggest that the mechanisms of species sorting and mass effects together control bacterioplankton communities, although mass effects exhibited higher contributions to community variation than species sorting. Given the importance of allochthonous bacteria input from various land use activities (i.e., mass effects), these results provide new insights into the environmental factors and determinant mechanisms that shape riverine ecosystem communities.

Keywords: bacterioplankton community; mass effects; riverine ecosystem; species sorting; variation partitioning.

FIGURE 1

Map of the Yuan River and sampling sites. The cities of Pingxiang, Yichun, and Xinyu are located upstream and downstream in the Yuan River basin.

FIGURE 2

Bacterioplankton community richness and diversity in wet and dry season communities, as measured with the (A) Chao1 richness index and (B) Shannon diversity index.

FIGURE 3

Cluster analysis and non-metric multidimensional scaling (NMDS) ordinations of Bray–Curtis distances among communities from 16 sampling sites. The numbers 1–16 correspond to the sampling sites Y01–Y16. Cluster analysis of bacterioplankton communities in the wet season (A) and dry season (B). Based on the NMDS plots (C), groups 1, 2, 3, and 4 are shown in blue and represent the five groups in the wet season. In addition, groups 1, 2, and 3 are shown in red and represent the three primary dry season groups. * indicates that the samples were collected in the region of the Jiangkou Reservoir.

FIGURE 4

Relative abundances of bacterioplankton communities from the wet and dry seasons, arranged from upstream to downstream. Compositions are shown as (A) bacterioplankton phyla distributions and (B) freshwater and non-freshwater bacterial OTU distributions. Taxa with relative abundances <1% are grouped as “others.”

FIGURE 5

RDA ordinations showing bacterioplankton community variation in relation to water chemistry parameters and land use types. The abundances of predominant phyla in the bacterioplankton communities were related to water chemistry parameters in wet (A) and dry (B) season samples and were also related to land use patterns in wet (C) and dry (D) season samples.

FIGURE 6

Bray–Curtis dissimilarity values of bacterioplankton communities in response to four geographic distance parameters. Relationships are shown between (A) river length (km), (B) catchment area (km²), (C) cumulative dendritic distance (km), and (D) mean dendritic stream length (km) with Bray–Curtis dissimilarity values for bacterioplankton communities. Pearson correlations (r) and probabilities (p) are shown to the right of each plot.

FIGURE 7

Partitioning of variation in bacterioplankton communities in the Yuan River. The effects on (A) wet season and (B) dry season communities were evaluated based on contributions from water chemistry, land use, and geographic distance parameters, following Supplementary Figure 2. Panels [a], [b], and [c] stand for the pure contribution of each explanatory matrix; panels [d], [f], and [e] stand for the joint contribution of two explanatory matrices; panel [g] stands for the joint contribution of three explanatory matrices. Unexplained: the variation not explained by the water chemistry, land use patterns or the geographic distance.