Extreme trophic tales: deciphering bacterial diversity and potential functions in oligotrophic and hypereutrophic lakes

Abstract

Background: Oligotrophy and hypereutrophy represent the two extremes of lake trophic states, and understanding the distribution of bacterial communities across these contrasting conditions is crucial for advancing aquatic microbial research. Despite the significance of these extreme trophic states, bacterial community characteristics and co-occurrence patterns in such environments have been scarcely interpreted. To bridge this knowledge gap, we collected 60 water samples from Lake Fuxian (oligotrophic) and Lake Xingyun (hypereutrophic) during different hydrological periods.

Results: Employing 16S rRNA gene sequencing, our findings revealed distinct community structures and metabolic potentials in bacterial communities of hypereutrophic and oligotrophic lake ecosystems. The hypereutrophic ecosystem exhibited higher bacterial α- and β-diversity compared to the oligotrophic ecosystem. Actinobacteria dominated the oligotrophic Lake Fuxian, while Cyanobacteria, Proteobacteria, and Bacteroidetes were more prevalent in the hypereutrophic Lake Xingyun. Functions associated with methanol oxidation, methylotrophy, fermentation, aromatic compound degradation, nitrogen/nitrate respiration, and nitrogen/nitrate denitrification were enriched in the oligotrophic lake, underscoring the vital role of bacteria in carbon and nitrogen cycling. In contrast, functions related to ureolysis, human pathogens, animal parasites or symbionts, and phototrophy were enriched in the hypereutrophic lake, highlighting human activity-related disturbances and potential pathogenic risks. Co-occurrence network analysis unveiled a more complex and stable bacterial network in the hypereutrophic lake compared to the oligotrophic lake.

Conclusion: Our study provides insights into the intricate relationships between trophic states and bacterial community structure, emphasizing significant differences in diversity, community composition, and network characteristics between extreme states of oligotrophy and hypereutrophy. Additionally, it explores the nuanced responses of bacterial communities to environmental conditions in these two contrasting trophic states.

Keywords: Bacterial diversity; Hypereutrophic; Lake Fuxian; Lake Xingyun; Network complexity and stability; Oligotrophic.

Fig. 1

(a) Geographical positioning of Lake Fuxian and Lake Xingyun within Yunnan Province, along with the delineation of sampling sites. (b) Upper: July 2020 images depicting Lake Xingyun’s water body covered with dense cyanobacterial blooms; Lower: Contrastingly, Lake Fuxian’s water body appears clear. (c) The comprehensive trophic level index (TLI) for Lake Fuxian and Lake Xingyun. TLI values indicate trophic states: TLI < 30 signifies oligotrophic, 30 ≤ TLI ≤ 50 denotes mesotrophic, 50 < TLI ≤ 60 implies mild eutrophic, 60 < TLI ≤ 70 indicates moderate eutrophic, and TLI > 70 represents hypereutrophic

Fig. 2

Comparison of the main environmental parameters between Lake Fuxian and Lake Xingyun. WT, water temperature; SD, Secchi disk transparency; DO, dissolved oxygen; Cond, conductivity; TN, total nitrogen; TDN, total dissolved nitrogen; NO₃-N, nitrate nitrogen; NH₄-N, ammonia nitrogen; TP, total phosphorus; TDP, total dissolved phosphorus; PO₄-P, phosphate phosphorus; COD_Mn, permanganate index; DOC, dissolved organic carbon; Chl-a, chlorophyll-a; SS, suspended solids; ISS, inorganic suspended solids; OM, organic matter content; TB, total bacterial abundance. The non-parametric Kruskal-Wallis test was performed to examine differences among the lakes. At the top of each boxplot: NS indicates no significant differences (P > 0.05); **, P < 0.01; ***, P < 0.001. In the boxplot, bold short black line and yellow dot denote the median and the mean of each parameter, respectively

Fig. 3

Comparisons of bacterial α-diversity and β-diversity between the oligotrophic Lake Fuxia and hypereutrophic Lake Xingyun, as well as among different hydrological periods. (a) Chao1 index, (b) Shannon index. ***, P < 0.001; **, P < 0.01; ns, no significant. (c) Nonmetric multidimensional scaling (NMDS) plot based on Bray–Curtis dissimilarity. Differences between bacterial community structures between the two lakes were tested using Analysis of Similarities (ANOSIM). The results are presented in the NMDS plot. Ellipses cover 95% of the data for each hydrological period

Fig. 4

Bacterial taxonomy of Lake Fuxian and Lake Xingyun, categorized at the (a) phylum and (b) class levels. Only the 10 most abundant taxa are included in the figure, while other rare taxa are grouped into “Others”. (c) The LEfSe cladogram shows significant differences in bacterial taxa between the two lakes. Colored dots on the cladogram denote taxa with noteworthy differences in abundance across lakes, while the cladogram circles delineate phylogenetic taxa from phylum to family

Fig. 5

Redundancy analyses (RDA) plots depict the prominent environmental varibles influencing variations in BCCs in both lakes (a) and within each specific lake, namely Lake Fuxian (c) and Lake Xingyun (d). The individual effects of each significant environmental variable are illustrated in figure (b). The significance levels: *P < 0.05, **P < 0.01, ***P < 0.001. The abbreviations used in the plots are as follows: COD_Mn, permanganate index; DOC, dissolved organic carbon; PO₄-P, phosphate phosphorus; NH₄-N, ammonia nitrogen; WT, water temperature; DO, dissolved oxygen; NO₃-N, nitrate nitrogen; TN, total nitrogen; TDP, total dissolved phosphorus; Cond, conductivity

Fig. 6

Mean proportion of bacterial functional groups with the significant difference (P < 0.05) between Lake Fuxian and Lake Xingyun

Fig. 7

Co-occurrence networks and their topological properties and stability of bacterial communities from Lake Fuxian (a) and Lake Xingyun (b). ASVs were selected based on their relative abundance (≥ 0.08%) among the total bacterial sequences. The size of each node is proportional to the number of connections to that node, and different colors indicate distinct modules within the networks. The distribution of bacterial taxa in each module from Lake Fuxian (c) and Lake Xingyun (d) is presented across different hydrological periods. To assess network stability, robustness (e) was quantified as the proportion of remaining taxa in a network after random removal of node (50%). Vulnerability (f) was determined by identifying the maximum node vulnerability in each network