Modeling lake conductivity in the contiguous United States using spatial indexing for big spatial data.

Conductivity is an important indicator of the health of aquatic ecosystems. We model large amounts of lake conductivity data collected as part of the United States Environmental Protection Agency's National Lakes Assessment using spatial indexing, a flexible and efficient approach to fitting spatial statistical models to big data sets. Spatial indexing is capable of accommodating various spatial covariance structures as well as features like random effects, geometric anisotropy, partition factors, and non-Euclidean topologies.

Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States.

Harmful algal blooms caused by cyanobacteria are a threat to global water resources and human health. Satellite remote sensing has vastly expanded spatial and temporal data on lake cyanobacteria, yet there is still acute need for tools that identify which waterbodies are at-risk for toxic cyanobacterial blooms. Algal toxins cannot be directly detected through imagery but monitoring toxins associated with cyanobacterial blooms is critical for assessing risk to the environment, animals, and people.

Denitrification and DNRA in Urban Accidental Wetlands in Phoenix, Arizona.

Denitrification and dissimilatory nitrate reduction to ammonium (DNRA) both require low oxygen and high organic carbon conditions common in wetland ecosystems. Denitrification permanently removes nitrogen from the ecosystem as a gas while DNRA recycles nitrogen within the ecosystem via production of ammonium. The relative prevalence of denitrification versus DNRA has implications for the fate of nitrate in ecosystems.