The Arctic is undergoing rapid environmental change, potentially affecting the physicochemical constraints of microbial communities that play a large role in both carbon and nutrient cycling in lacustrine environments. However, the microbial communities in such Arctic environments have seldom been studied, and the drivers of their composition are poorly characterized. To address these gaps, we surveyed the biologically active surface sediments in Lake Hazen, the largest lake by volume north of the Arctic Circle, and a small lake and shoreline pond in its watershed. High-throughput amplicon sequencing of the 16S rRNA gene uncovered a community dominated by Proteobacteria, Bacteroidetes, and Chloroflexi, similar to those found in other cold and oligotrophic lake sediments. We also show that the microbial community structure in this Arctic polar desert is shaped by pH and redox gradients. This study lays the groundwork for predicting how sediment microbial communities in the Arctic could respond as climate change proceeds to alter their physicochemical constraints.
Keywords: arctic lakes; high-throughput sequencing; lake sediments; machine learning; microbial community composition; microbial diversity.