| Summary: | Understanding spatial and temporal heterogeneity in ecosystems is essential to forecasting the effects of environmental changes. Freshwater microbes, including cyanobacteria, play a crucial role in food-web structures and biochemical processes, yet can exhibit substantial heterogeneity through space and time. They also act as powerful indicators of natural and human-induced stress due to their high metabolic and rapid response to environmental change. The formation of cyanobacteria blooms can be particularly important due to the potential production of toxins that are harmful to humans and wildlife. While high water temperatures and high nutrients are largely recognized as triggers of cyanobacterial bloom formation, there is growing evidence of the role of its associated microbiome in bloom formation. The inability to accurately forecast cyanobacteria blooms is challenged by uncertainty in the degree to which microbial diversity, and bloom forming taxa in particular, exhibit spatial heterogeneity and how spatial heterogeneity varies seasonally or between lakes spanning the trophic gradient. Here, we used long-read sequencing of the 16S rRNA gene to quantify variations in microbial spatiotemporal dynamics over the course of an ice-free season between two lakes that varied substantially in trophic status. Our results showed that the microbial community composition of eutrophic Chautauqua Lake was seasonally and spatially structured; however, during bloom events we observed lower diversity and a homogeneous community dominated by Microcystis and enriched with Gammaproteobacteria. In oligotrophic Lake George, seasonality rather than the basin of origin played a major role in structuring the microbial community; however, there was a significant difference between basins when controlling for the temporal effect and was linked to a South-to-North anthropogenic gradient. This study provides a solid foundation for exploiting long-read sequencing of prokaryotes and couples sequencing with traditional water quality monitoring to assess microbial dynamics (e.g., cyanobacteria bloom microbiome) and the effect of local and global stressors.
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