Mary Anne Evans, PhD

Mary Anne Evans is a Research Ecologist at the USGS Great Lakes Science Center where she contributes to the Restoration and Conservation Science branch. Her current research focuses on drivers and consequences of harmful and nuisance algal blooms (HABs and NABs) including nutrient, light, and dreissenid influences on benthic algal blooms, cyanobacterial HABs in western Lake Erie, nutrient and algal dynamics in other river influenced nearshore areas of the Great Lakes, and translating HABs and NABs science for diverse audiences. Prior to joining the USGS, she studied hypoxic “dead zones” in Chesapeake Bay and the Gulf of Mexico as a post-doctoral scientist at the University of Michigan School of Natural Resources and Environment and recreational lakes in Michigan as a post-doctoral scientist at Michigan State University. She received her Ph.D. in biology from the University of Michigan, studying the phytoplankton ecology of arctic lakes.

Research Summary 

My research explores the interaction of physical and biological processes that control ecosystem functions. I am especially interested in controls of phytoplankton and benthic algae growth. Understanding the regulation of primary producers is integral to predicting, managing, and conserving ecosystem function, especially for systems impacted by both local human stresses and global climate change. In addition, large accumulations of algal biomass, either of toxic species or leading to decomposition induced hypoxia, can be detrimental to human use of aquatic resources. The conditions necessary for such harmful algal blooms (HABs) and hypoxic events are predicted to increase under climate change scenarios; thus, understanding their controls will be increasingly important to ecosystem management. 

My approach to research is to combine field studies with mathematical modeling. I use numerical models to explore a broader range of mechanistic variability than is practical in field experiments, while, at the same time, I use field data to test model predictions. In this way, each approach informs the other, allowing for more robust and general conclusions that may be extrapolated across aquatic ecosystems.