PS 51-51 - Examining spatial and temporal trends in precipitation-borne microbial communities: A novel quantification across the United States

Thursday, August 10, 2017
Exhibit Hall, Oregon Convention Center
Aurora L. H. Bayless-Edwards1, Ken Aho1, Carolyn Weber1, Jason T. Werth1, Boris Vinatzer2, David G. Schmale III2, Brent C. Christner3, Rachel Joyce3 and Heather Lavender3, (1)Department of Biological Sciences, Idaho State University, Pocatello, ID, (2)Department of Plant Pathology, Physiology, and Weed Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, (3)Department of Microbiology and Cell Science, University of Florida, Gainesville, FL

Airborne microbes, including bacteria and fungi, comprise an important component of the biosphere, and may have an active role in atmospheric processes. For example, species that produce ice-nucleating proteins may affect ice crystal formation in clouds, allowing microbial transport in precipitation. New research indicates that the geographic distribution of airborne bacterial communities is influenced by both season and the origin of the precipitation event. However, a comprehensive description of cloud-dwelling microbial communities is far from complete.

To investigate fungal communities deposited during precipitation, rain events were sampled in three states (Virginia, Idaho, and Louisiana) across seasons. Operational Taxonomic Unit (OTU) level community composition of these samples was determined using culture-free amplifications of the V9 hypervariable region of the 18s fungal small subunit rRNA gene. Raw reads were processed, and OTUs were assigned using the MOTHUR pipeline. We used Non-Metric Multidimensional Scaling (NMDS) to visualize rain event community differences by site and season. Samples were clustered using Unweighted Pair Group Method with Arithmetic Mean (UPMGA) linkage, after careful consideration of the efficacy of seven other cluster analysis methods. The optimal number of UPMGA clusters was determined using five geometric and non-geometric evaluators.


The optimal UPMGA classification contained 8 clusters. Idaho samples formed two distinct clusters split between seasons (winter and summer/spring). Louisiana samples formed three distinct clusters with no temporal trend, and two mixed clusters that also contained samples from Virginia. Both mixed clusters were predominately composed of Louisiana winter and autumn samples, grouping with samples collected at the Virgina Tech Kentland Farm across seasons. The final cluster was composed entirely of samples collected at the Virginia Tech main campus in Blacksburg, VA.

This project provides novel quantification of spatial and temporal variation found in atmospheric fungal communities. These findings are an important first step towards quantifying patterns in fungal transport and dispersal. Spatiotemporal variation in community composition is indicative of selective pressures that might favor one community over another. Our findings call for further investigation of abiotic and biotic constraints, which may be influencing these highly mobile atmospheric microbial communities.