Comparisons of the composition and biogeographic distribution of the bacterial communities occupying South African thermal springs with those inhabiting deep subsurface fracture water - Magnabosco, Cara; Tekere, Memory; Lau, Maggie C. Y.; Linage, Borja; Kuloyo, Olukayode; Erasmus, Mariana; Cason, Errol; van Heerden, Esta; Borgonie, Gaetan; Kieft, Thomas L.; Olivier, Jana; Onstott, Tullis C. (Frontiers in Microbiology, 2014)
South Africa has numerous thermal springs that represent topographically driven meteoric water migrating along major fracture zones. The temperature (40-70°C) and pH (8-9) of the thermal springs in the Limpopo Province are very similar to those of the low salinity fracture water encountered in the South African mines at depths ranging from 1.0 to 3.1 km. The major cation and anion composition of these thermal springs are very similar to that of the deep fracture water with the exception of the dissolved inorganic carbon and dissolved O2, both of which are typically higher in the springs than in the deep fracture water. The in situ biological relatedness of such thermal springs and the subsurface fracture fluids that feed them has not previously been evaluated. In this study, we evaluated the microbial diversity of six thermal spring and six subsurface sites in South Africa using high-throughput sequencing of 16S rRNA gene hypervariable regions. Proteobacteria were identified as the dominant phylum within both subsurface and thermal spring environments, but only one genera, Rheinheimera, was identified among all samples. Using Morisita similarity indices as a metric for pairwise comparisons between sites, we found that the communities of thermal springs are highly distinct from subsurface datasets. Although the Limpopo thermal springs do not appear to provide a new window for viewing subsurface bacterial communities, we report that the taxonomic compositions of the subsurface sites studied are more similar than previous results would indicate and provide evidence that the microbial communities sampled at depth are more correlated to subsurface conditions than geographical distance.