Humans have an indelible impact on the natural world, and our ability to understand and mitigate the effects of anthropogenic stressors on the environment will decide its fate. Our understanding of how our anthropogenic influence has ecosystem-wide effects is based on observational and empirical data, usually at one level of biological organization; for example, censorship of groups of animals in their natural environment gives data at the population level while lab experiments usually produce data at the suborganismal (e.g. genetic) or individual level. Therefore, our ability to understand rather than merely observe the response of the environment to anthropogenic stress relies on our ability to extrapolate between levels of biological organization. My research is broadly focused on this fundamental ecological question: can we accurately predict the effect of a stressor on populations of organisms using suborganismal or individual-level data? I utilize empirical and theoretical methods to study the effect of contaminants on freshwater ecosystems.
A central theme of my research aims to quantify what constitutes “ecological risk” in pollution regulation. Ecological Risk Assessment (ERA) is charged with predicting the effect of a stressor on the environment, and environmental regulators develop an ERA to decide the maximum allowable concentration of a potential contaminant in the environment that will not cause harm. My research focuses on improving how ecological risk is measured at the individual level and extrapolated to the population level.
I develop quantitative models to guide an empirical regime and extrapolate results to untested environments. My research focuses on the effects of contaminants on freshwater organisms and identifying the important feedbacks that occur at the population level with the goal to accurately predict population-level effects of a stressor using individual-level and suborganismal data.