Statistical determination of crucial taxa indicative of pollution gradients in sediments of Lake Taihu, China.

In order to accurately monitor the changes in a freshwater ecosystem in response to anthropogenic stressors, microbe-environment correlations and microbe-microbe interactions were combined to determine crucial indicator taxa in contaminated sediments. The diversity, composition, and co-occurrence pattern of bacterial communities in 23 sediment samples collected from Lake Taihu were explored using 16S rRNA amplicon sequencing analysis. Fisher's exact test showed that the cluster analyses of samples could show a direct correlation between the relative abundance of bacterial communities and the physicochemical properties of the sediment (P < 0.0001), suggesting that bacterial communities can be used to monitor contamination gradients in freshwater sediments. According to the microbe-environment correlation, 24 orders and 60 families were initially identified via indicator species analysis as indicator taxa of different pollution levels. The co-occurrence network further showed that topological features of bacterial communities were clearly different at different pollution levels, although the diversity and composition of bacterial communities displayed similarities between minimally and moderately polluted sites. Indicator taxa were then screened for keystone species, which co-occurrence relationships showed the high degree and low betweenness centrality values (i.e. degree >5, betweenness centrality <1000) of the network. Nine orders and 13 families were finally extracted as crucial indicator taxa of the different pollution levels in eutrophic Lake Taihu. Obtaining crucial indicator taxa from environmental sequences allows to trace increasing levels of pollution in aquatic ecosystems and provides a novel mean to monitor watersheds sensitive to anthropic influences.