Abstract
In this study, we investigated the role of mineral-bound P and Fe in defining microbial abundance and diversity in a carbon-rich groundwater. Field colonization experiments of initially sterile mineral surfaces were combined with community structure characterization of the attached microbial population. Silicate minerals containing varying concentrations of P (∼1000 ppm P) and Fe (∼4 wt % Fe 2 O3), goethite (FeOOH), and apatite [Ca5(PO4)3(OH)] were incubated for 14 months in three biogeochemically distinct zones within a petroleum-contaminated aquifer. Phospholipid fatty acid analysis of incubated mineral surfaces and groundwater was used as a measure of microbial community structure and biomass. Microbial biomass on minerals exhibited distinct trends as a function of mineralogy depending on the environment of incubation. In the carbon-rich, aerobic groundwater attached biomass did not correlate to the P- or Fe- content of the mineral. In the methanogenic groundwater, however, biomass was most abundant on P-containing minerals. Similarly, in the Fe-reducing groundwater a correlation between Fe-content and biomass was observed. The community structure of the mineral-adherent microbial population was compared to the native groundwater community. These two populations were significantly different regardless of mineralogy, suggesting differentiation of the planktonic community through attachment, growth, and death of colonizing cells. Biomarkers specific for dissimilatory Fe-reducing bacteria native to the aquifer were identified only on Fe-containing minerals in the Fe-reducing groundwater. These results demonstrate that the trace nutrient content of minerals affects both the abundance and diversity of surface-adherent microbial communities. This behavior may be a means to access limiting nutrients from the mineral, creating a niche for a particular microbial population. These results suggest that heterogeneity of microbial populations and their associated activities in subsurface environments extend to the microscale and cautions over-interpretation of highly sample-dependent measurements in the context of interpreting field data.
Published Version
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