Sulfur-Cycling in Methane-Rich Ecosystems: Uncovering Microbial Processes and Novel Niches

Microbial sulfur cycling communities were investigated in two methane-rich ecosystems, terrestrial mud volcanoes (TMVs) and marine methane seeps, in order to investigate niches and processes that would likely be central to the functioning of these crucial ecosystems. Terrestrial mud volcanoes represent geochemically diverse habitats with varying sulfur sources and yet sulfur-cycling in these environments remains largely unexplored. Here we characterized the sulfur-metabolizing microorganisms and activity in 4 TMVs in Azerbaijan, supporting the presence of active sulfur-oxidizing and sulfate-reducing guilds in all 4 TMVs across a range of physiochemical conditions, with diversity of these guilds being unique to each TMV. We also found evidence for the anaerobic oxidation of methane coupled to sulfate reduction, a process which we explored further in the more tractable marine methane seeps. Diverse associations between methanotrophic archaea (ANME) and sulfate-reducing bacterial groups (SRB) often co-occur in marine methane seeps, however the ecophysiology of these different symbiotic associations has not been examined. Using a combination of molecular, geochemical and fluorescence in situ hybridization coupled to nano-scale secondary ion mass spectrometry (FISH-NanoSIMS) analyses of in situ seep sediments and methane-amended sediment incubations from diverse locations, we show that the unexplained diversity in SRB associated with ANME cells can be at least partially explained by preferential nitrate utilization by one particular partner, the seepDBB. This discovery reveals that nitrate is likely an important factor in community structuring and diversity in marine methane seep ecosystems. The thesis concludes with a study of the dynamics between ANME and their associated SRB partners. We inhibited sulfate reduction and followed the metabolic processes of the community as well as the effect of ANME/SRB aggregate composition and growth on a cellular level by tracking 15N substrate incorporation into biomass using FISH-NanoSIMS. We revealed that while sulfate-reducing bacteria gradually disappeared over time in incubations with an SRB inhibitor, the ANME archaea persisted in the form of ANME-only aggregates, which are capable of little to no growth when sulfate reduction is inhibited. These data suggest ANME are not able to synthesize new proteins when sulfate reduction is inhibited.

The search for gravitational waves from the coalescence of black hole binary systems in data from the LIGO and Virgo detectors. Or: A dark walk through a random forest

The LIGO and Virgo gravitational-wave observatories are complex and extremely sensitive strain detectors that can be used to search for a wide variety of gravitational waves from astrophysical and cosmological sources. In this thesis, I motivate the search for the gravitational wave signals from coalescing black hole binary systems with total mass between 25 and 100 solar masses. The mechanisms for formation of such systems are not well-understood, and we do not have many observational constraints on the parameters that guide the formation scenarios. Detection of gravitational waves from such systems — or, in the absence of detection, the tightening of upper limits on the rate of such coalescences — will provide valuable information that can inform the astrophysics of the formation of these systems. I review the search for these systems and place upper limits on the rate of black hole binary coalescences with total mass between 25 and 100 solar masses. I then show how the sensitivity of this search can be improved by up to 40% by the the application of the multivariate statistical classifier known as a random forest of bagged decision trees to more effectively discriminate between signal and non-Gaussian instrumental noise. I also discuss the use of this classifier in the search for the ringdown signal from the merger of two black holes with total mass between 50 and 450 solar masses and present upper limits. I also apply multivariate statistical classifiers to the problem of quantifying the non-Gaussianity of LIGO data. Despite these improvements, no gravitational-wave signals have been detected in LIGO data so far. However, the use of multivariate statistical classification can significantly improve the sensitivity of the Advanced LIGO detectors to such signals.