Quantitative Mapping of Archaeal Biodiversity on the Geochemical Landscape in Yellowstone National Park, Wyoming

Quantifying the constraints imposed by the environment on microbial community structure, composition, and function is a major frontier in biogeoscience research since such information can be used to predict how microbial communities will respond to environmental change at the compositional and functional level. The strong physical and chemical gradients and the relatively simple microbial diversity associated with geothermal environments makes them model environments for the development and application of techniques capable of quantifying the extent of such relationships. Here, we present the results of an integrated study of the genetics, lipids, and geochemistry from 40 geochemically- and geographically-distinct geothermal environments in Yellowstone National Park (YNP), Wyoming, USA. Ecological modeling tools were used to quantify the links between the structure, composition, and abundance of archaeal 16S rRNA genes, archaeal ammonia monoxygenase genes, core and intact polar glycerol dibiphytanyl glycerol tetraether (GDGT) lipids, and a number of physical and chemical measurements. The abundance of a number of 16S rRNA gene lineages was correlated with the abundance of lipids hypothesized to be synthesized by those lineages. The abundance of other uncharacterized and novel 16S rRNA gene lineages also exhibited strong correlations with individual lipids, suggesting that these organisms may be the source of these lipids in the natural environment. Collectively, these results suggest that both archaeal 16S rRNA genes and lipids are non-randomly distributed across the YNP geothermal landscape and that their distribution and composition can be predicted on the basis of geochemical and physical measurements. These quantitative results underscore the utility of modeling tools in understanding how environment drives the diversification of life both at the taxonomic and functional level, information which will be required to predict how ecosystems change in composition and function in response to global climate change.