Optode-based chemical imaging of laboratory burned soil reveals millimeter-scale heterogeneous biogeochemical responses.

Soil spatial responses to fire are unclear. Using optical chemical sensing with planar 'optodes', pH and dissolved O2 concentration were tracked spatially with a resolution of 360 µm per pixel for 72 h after burning soil in the laboratory with a butane torch (∼1300 °C) and then sprinkling water to simulate a postfire moisture event. Imaging data from planar optodes correlated with microbial activity (quantified via RNA transcripts). Post-fire and post-wetting, soil pH increased throughout the entire ∼13 cm × 17 cm × 20 cm rectangular cuboid of sandy loam soil. Dissolved O2 concentrations were not impacted until the application of water postfire. pH and dissolved O2 both negatively correlated (p < 0.05) with relative transcript expression for galactose metabolism, the degradation of aromatic compounds, sulfur metabolism, and narH. Additionally, dissolved O2 negatively correlated (p < 0.05) with the relative activity of carbon fixation pathways in Bacteria and Archaea, amoA/amoB, narG, nirK, and nosZ. nifH was not detected in any samples. Only amoB and amoC correlated with depth in soil (p < 0.05). Results demonstrate that postfire soils are spatially complex on a mm scale and that using optode-based chemical imaging as a chemical navigator for RNA transcript sampling is effective.

Statistical Learning and Uncommon Soil Microbiota Explain Biogeochemical Responses after Wildfire.

Wildfires are a perennial event globally, and the biogeochemical underpinnings of soil responses at relevant spatial and temporal scales are unclear. Soil biogeochemical processes regulate plant growth and nutrient losses that affect water quality, yet the response of soil after variable intensity fire is difficult to explain and predict. To address this issue, we examined two wildfires in Colorado, United States, across the first and second postfire years and leveraged statistical learning (SL) to predict and explain biogeochemical responses. We found that SL predicts biogeochemical responses in soil after wildfire with surprising accuracy. Of the 13 biogeochemical analytes analyzed in this study, 9 are best explained with a hybrid microbiome + biogeochemical SL model. Biogeochemical-only models best explain 3 features, and 1 feature is explained equally well with the hybrid and biogeochemical-only models. In some cases, microbiome-only SL models are also effective (such as predicting NH4+). Whenever a microbiome component is employed, selected features always involve uncommon soil microbiota (i.e., the "rare biosphere" [existing at <1% mean relative abundance]). Here, we demonstrate that SL paired with DNA sequence and biogeochemical data predicts environmental features in postfire soils, although this approach could likely be applied to any biogeochemical system. IMPORTANCE Soil biogeochemical processes are critical to plant growth and water quality and are substantially disturbed by wildfire. However, soil responses to fire are difficult to predict. To address this issue, we developed a large environmental data set that tracks postfire changes in soil and used statistical learning (SL) to build models that exploit complex data to make predictions about biogeochemical responses. Here, we show that SL depends upon uncommon microbiota in soil (the "rare biosphere") to make surprisingly accurate predictions about soil biogeochemical responses to wildfire. Using SL to explain variation in a natively chaotic environmental system is mechanism independent. Likely, the approach that we describe for combining SL with microbiome and biogeochemical parameters has practical applications across a range of issues in the environmental sciences where predicting responses would be useful.

Spatial and Temporal Constraints on the Composition of Microbial Communities in Subsurface Boreholes of the Edgar Experimental Mine.

The deep biosphere hosts uniquely adapted microorganisms overcoming geochemical extremes at significant depths within the crust of the Earth. Attention is required to understand the near subsurface and its continuity with surface systems, where numerous novel microbial members with unique physiological modifications remain to be identified. This surface-subsurface relationship raises key questions about networking of surface hydrology, geochemistry affecting near-subsurface microbial composition, and resiliency of subsurface ecosystems. Here, we apply molecular and geochemical approaches to determine temporal microbial composition and environmental conditions of filtered borehole fluid from the Edgar Experimental Mine (∼150 m below the surface) in Idaho Springs, CO. Samples were collected over a 4-year collection period from expandable packers deployed to accumulate fluid in previously drilled boreholes located centimeters to meters apart, revealing temporal evolution of borehole microbiology. Meteoric groundwater feeding boreholes demonstrated variable recharge rates likely due to a complex and undefined fracture system within the host rock. 16S rRNA gene analysis determined that unique microbial communities occupy the four boreholes examined. Two boreholes yielded sequences revealing the presence of Desulfosporosinus, Candidatus Nitrotoga, and Chelatococcus associated with endemic subsurface communities. Two other boreholes presented sequences related to nonsubsurface-originating microbiota. High concentration of sulfate along with detected sulfur reducing and oxidizing microorganisms suggests that sulfur related metabolic strategies are prominent within these near-subsurface boreholes. Overall, results indicate that microbial community composition in the near-subsurface is highly dynamic at very fine spatial scales (<20 cm) within fluid-rock equilibrated boreholes, which additionally supports the role of a relationship for surface geochemical processes infiltrating and influencing subsurface environments. IMPORTANCE The Edgar Experimental Mine, Idaho Springs, CO, provides inexpensive and open access to borehole investigations for subsurface microbiology studies. Understanding how microbial processes in the near subsurface are connected to surface hydrological influences is lacking. Investigating microbial communities of subsurface mine boreholes provides evidence of how geochemical processes are linked to biogeochemical processes within each borehole and the geochemical connectedness and mobility of surface influences. This study details microbial community composition and fluid geochemistry over spatial and temporal scales from boreholes within the Edgar Mine. These findings are relevant to biogeochemistry of near-surface mines, caves, and other voids across planetary terrestrial systems. In addition, this work can lead to understanding how microbial communities relate to both fluid-rock equilibration, and geochemical influences may enhance our understanding of subsurface molecular biological tools that aid mining economic practices to reflect biological signals for lucrative veins in the near subsurface.

Regional fresh snowfall microbiology and chemistry are driven by geography in storm-tracked events, Colorado, USA.

Snowfall is a global phenomenon highly integrated with hydrology and ecology. Forays into studying bioaerosols and their dependence on aeolian movement are largely constrained to either precipitation-independent analyses or in silico models. Though snowpack and glacial microbiological studies have been conducted, little is known about the biological component of meteoric snow. Through culture-independent phylogenetic and geochemical analyses, we show that the geographical location at which snow precipitates determines snowfall's geochemical and microbiological composition. Storm-tracking, furthermore, can be used as a valuable environmental indicator to trace down what factors are influencing bioaerosols. We estimate annual aeolian snowfall deposits of up to ∼10 kg of bacterial/archaeal biomass per hectare along our study area of the eastern Front Range in Colorado. The dominant kinds of microbiota captured in an analysis of seven snow events at two different locations, one urban, one rural, across the winter of 2016/2017 included phyla Proteobacteria, Bacteroidetes, Firmicutes, and Acidobacteria, though a multitude of different kinds of organisms were found in both. Taxonomically, Bacteroidetes were more abundant in Golden (urban plain) snow while Proteobacteria were more common in Sunshine (rural mountain) samples. Chemically, Golden snowfall was positively correlated with some metals and anions. The work also hints at better informing the "everything is everywhere" hypotheses of the microbial world and that atmospheric transport of microbiota is not only common, but is capable of disseminating vast amounts of microbiota of different physiologies and genetics that then affect ecosystems globally. Snowfall, we conclude, is a significant repository of microbiological material with strong implications for both ecosystem genetic flux and general bio-aerosol theory.