Spatial variability in airborne bacterial communities across land-use types and their relationship to the bacterial communities of potential source environments

Although bacteria are ubiquitous in the near-surface atmosphere and they can have important effects on human health, airborne bacteria have received relatively little attention and their spatial dynamics remain poorly understood. Owing to differences in meteorological conditions and the potential sources of airborne bacteria, we would expect the atmosphere over different land-use types to harbor distinct bacterial communities. To test this hypothesis, we sampled the near-surface atmosphere above three distinct land-use types (agricultural fields, suburban areas and forests) across northern Colorado, USA, sampling five sites per land-use type. Microbial abundances were stable across land-use types, with ∼105–106 bacterial cells per m3 of air, but the concentrations of biological ice nuclei, determined using a droplet freezing assay, were on average two and eight times higher in samples from agricultural areas than in the other two land-use types. Likewise, the composition of the airborne bacterial communities, assessed via bar-coded pyrosequencing, was significantly related to land-use type and these differences were likely driven by shifts in the sources of bacteria to the atmosphere across the land-uses, not local meteorological conditions. A meta-analysis of previously published data shows that atmospheric bacterial communities differ from those in potential source environments (leaf surfaces and soils), and we demonstrate that we may be able to use this information to determine the relative inputs of bacteria from these source environments to the atmosphere. This work furthers our understanding of bacterial diversity in the atmosphere, the terrestrial controls on this diversity and potential approaches for source tracking of airborne bacteria

Integrating Archaeological Theory and Predictive Modeling: a Live Report from the Scene

Archaeological predictive modeling has been used successfully for over20 years as a decision-making tool in cultural resources management. Itsappreciation in academic circles however has been mixed because of its perceivedtheoretical poverty. In this paper, we discuss the issue of integrating currentarchaeological theoretical approaches and predictive modeling. We suggest amethodology for doing so based on cognitive archaeology, middle range theory,and paleoeconomic modeling. We also discuss the problems associated with testingpredictive models