Microbial communities drive the biogeochemical cycling of carbon, nitrogen, and phosphorus in lakes, however their community structure and how it changes over time is poorly understood. Lakes are terrestrial hot spots of carbon and nutrient cycling, so a fuller understanding of their bacterial community dynamics will increase our understanding of these important biogeochemical processes. This poster analyses a 16S rRNA gene iTAG time series spanning the years 2000-2010 that includes 95 samples taken from the epilimnion of eutrophic Lake Mendota, Madison, WI. This iTAG dataset includes 17,437 OTUs, which were simplified using a deblurring algorithm to remove likely sequencing errors. Taxonomy was assigned to OTUs using a custom curated training set for Lake Mendota followed by Green Genes. The bacterial communities display consistent annual patterns, but differ in year 2006 when the spring diatom bloom was unusually low. Top bacterial clades include acI-A, acI-B, acTH1-A, betI-A, bacI-A, alfV-A, Luna1-A, and Pnec. OTUs and taxa were grouped by their cooccurrence patterns to further examine bacterial community structure and the relationship between taxonomy and ecological function. Grouping was performed with hierarchical clustering using proportional relationships as opposed to correlation or distance matrices. Correlation and distance measures depend on which subsets of OTUs are included, while proportionality does not. Therefore these analyses are robust to the somewhat arbitrary decisions about how many of the OTUs to include. Grouping at different taxonomic levels can clarify the ecological significance of taxonomy. For example, organisms that fill the same ecological niche may compete when examined at the lowest taxa level, but may behave coherently when grouped at a higher level. This analysis focuses on the ecological significance of microbial community changes over time.