Stratification in Microbial Communities with Depth and Redox Status in a Eutrophic Lake Across Two Years

Abstract

Bacteria have a profound impact on many key biogeochemical cycles in freshwater lake ecosystems; in turn, the composition of bacteria in the lake is contingent on the chemistry of the water. Many parameters that affect bacterial growth in freshwater ecosystems, such as water temperature, nutrient levels, and redox status, exhibit notable inter-annual differences in addition to seasonal changes. However, little is known about the impact of these inter- and intra-annual differences on the freshwater microbiome, especially in anoxic bottom waters. In this study, we paired biogeochemical field data with 16S rRNA gene amplicon sequencing of depth-discrete samples from a dimictic lake across two open-water seasons to observe variation in the microbiome relative to differences in water chemistry between two years. We found differences in the timing anoxia onset and the redox status in the water column across the two years. Changes in redox status led to major shifts in the microbial community composition. While there was little variation between years in the microbial taxonomic composition at the phyla level, there was substantial interannual variation at more resolved taxonomic levels. Some interannual differences can be explained by links between the predicted metabolic potential of those lineages and the different redox conditions between the two years. These results emphasize the need for repeated monitoring to deduce long-term trends in microbial communities in natural ecosystems and the importance of a comprehensive evaluation of environmental conditions contemporary with any microbiome analysis. ImportanceThe results of this study add to the growing body of evidence that microbial communities in natural systems are temporally dynamic on multiple scales, and even more so at highly resolved taxonomic levels. By correlating our analysis of the microbial community with the redox status of the water column we find that many community differences between the years can be in part explained by these parameters. As collecting 16S rRNA data over many years is critical to understanding long term trends in microbial ecology, our study suggests that corresponding water chemistry data could be a powerful tool to help explain microbiome trends.