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Biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ environmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on environmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive., IMPORTANCE Here we show that DNA from natural bacterial communities can be used as a quantitative biosensor to accurately distinguish unpolluted sites from those contaminated with uranium, nitrate, or oil. These results indicate that bacterial communities can be used as environmental sensors that respond to and capture perturbations caused by human impacts.

Peach [Prunus persica (L.) Batsch.] production in the southeastern United States. has suffered a decline in the past several decades due to late and severe spring freezes. Much of the peach production in Tennessee is located on topographically diverse sites. Peach producers do not have adequate information about the effects of microclimates within their orchards on peach bud development and subsequent freeze risk. Such information may help them select the best adapted cultivars based on microclimate. The objectives of this study were to use a `Redhaven' peach bud development model to compare and contrast the phenology and freeze risks at five sites within the orchard, and to study the freeze characteristics (advective vs. radiative) of these same sites. Automated weather stations were located in topoclimatically different areas of a peach orchard in Dandridge, Tenn., from October 1990 to April 1992. Hourly weather data included average air temperature, chill units (CUs), growing degree hours (GDHs), and wind speed. The data show that the microclimatic factors that affect peach bud development and freeze risk, such as CUs, GDHs, and minimum temperatures, can vary in a topographically diverse orchard, but the effects are not as straightforward as previously reported. For instance, peach buds at a site at a lower elevation may break dormancy later due to slower accumulation of CUs, which could be beneficial to the grower. Then the buds may develop more slowly due to cold air drainage to the lower elevations, which would also be desirable. However, the lower temperatures due to cold air drainage could be damaging to the bud and cause more problems with injuries. Over the two seasons, there were more radiative freezes than advective freezes, but the advective freezes were more severe. Small differences in wind speed can account for surprisingly different freeze conditions during radiative situations. Any improvement in cold air drainage should help decrease the duration and severity of radiative freezes.

Tennessee is located in an area of diverse topography, ranging in elevation from