Data_Sheet_4_Spatiotemporal Controls on the Urban Aerobiome.CSV

Greater exposure to environmental microorganisms has been hypothesized to reduce the likelihood of developing autoimmune disorders, and vegetation is known to be a source of diverse microbiota to the air. However, the spatiotemporal dynamics of airborne microbial communities in urban environments with varying amounts and types of vegetation are poorly understood. In this study we used high-throughput sequencing of the bacterial 16S rRNA gene to assess whether fine-scale variation in urban vegetation influences the diversity, composition, or structure of airborne bacterial communities over time. We used passive settling dishes to collect airborne bacteria from 36 sites representing three urban land cover types (forest, grassland, paved) over a 3-month period in Eugene-Springfield, Oregon, USA. We used remote sensing data (aerial 4-band orthoimagery and LiDAR) and geographic information systems (GIS) to assess detailed site characteristics (e.g., total vegetation cover and structural diversity) for each site. Our initial analysis indicated that site was the most important factor explaining variation in bacterial community structure (R2 = 0.32, p < 0.001), followed by sampling date (R2 = 0.24, p < 0.001), while land cover type was a significant but weak predictor (R2 = 0.06, p < 0.001) and other vegetation metrics were even less predictive. However, when samples were analyzed separately by date, the explanatory power of land cover type increased substantially; six of nine dates showed significant effects (p < 0.05) with R2 ranging from 0.16–0.31, indicating that land cover type had a marked influence on bacterial community structure that was obscured by the effects of site and sampling date. Despite the importance of site as a predictor of bacterial community structure, Mantel tests for spatial correlation were insignificant for most sampling dates, suggesting that localized site characteristics were driving this relationship. We use our results to propose a space-time conceptual model of the interactions between site-scale environmental features (e.g., vegetation characteristics) and regional-scale temporal processes and events (e.g., agricultural harvesting) to understand and perhaps manage intraurban airborne bacterial communities.