Research Program

Throughout my entire scientific career, I have investigated microbial metabolisms in a bottom-up and top-down fashion to uncover the links between genes and metabolic traits in an evolutionary context, and therefore understand ecosystem functioning.  My research converges at the intersection of several disciplines such as microbial ecology, genomics, molecular biology, evolution, geology, computational biology, and biochemistry.

The main goal of my doctoral dissertation was to identify microbial community patterns that can be used as bioindicators to signal changes in ecosystem state caused by anthropogenic environmental disturbance. My dissertation research included four chapters to achieve this goal. 

The focus of chapter two was to identify a microbial mat ecological model that can be used to understand patterns in key metabolical or ecological processes under environmental stress. Chapter three was a literature review and data synthesis effort to describe the anthropogenic pressures occurring at our study site, Cuatro Cienégas Basin (CCB), a naturally isolated valley in the Chihuahuan Desert (Coahuila) in Mexico. CCB is a World Wildlife Fund (WWF) hotspot for biodiversity and an internationally recognized wetland. However, water extraction to meet the growing demands of irrigation for agriculture has drastically decreased lagoon water levels in recent years. This region is now facing extreme desiccation, which exerts physical and chemical stresses on the biota thriving in lagoon ecosystems. Here I highlight the value of using network inference approaches to understand the response of microbial communities to environmental perturbation. In chapter four, I used time-series, spatially-resolved, analysis to understand the relationships among microbial taxa and their metabolic capabilities in lagoons undergoing desiccation. This computational approach synthesized tools developed over the course of my dissertation: (1) an algorithm to capture the fluctuations of key biogeochemical cycles over time; (2) information about the microbial metabolic pathways inherent to microbial mat ecology; and (3) network inference to examine the relationships between microbial taxa under environmental stress.