Theoretical ecology can be used to explain microbial community organization and evolution, such as in the stratified layers of stromatolites, the oldest representation of microbial communities. In my current work, I am exploring how microbial models can be linked to fluid dynamics to predict the mortality and transport of fecal indicator bacteria in the Hudson River.
Mathematical models and remote sensing both offer cost effective methods for analyzing water quality. I recently applied to the NASA Earth & Space Science Fellowship in hopes of gaining funding to relate reflectance data captured by Landsat and MODIS satellites to particle associated microbes. In the Hudson River, fecal indicator bacteria concentrations are highly correlated to particle concentrations, which suggests that turbidity measured via satellite could be used as a proxy for microbial contamination. By combining long-term data on turbidity and microbial concentrations with satellite data, I will create an algorithm that can be used to predict water quality in various urban, coastal regions.
focus: Oceanography & Microbial Ecology
focus: Mathematical Biology