Research Themes
Predictive Disease Ecology
Our goal is to develop early warning strategies for vector-borne diseases that affect livestock in the US. To accomplish this goal, we developed a conceptual and operational framework for predictive disease ecology requiring big data and trans-disciplinary scientific expertise. Our approach is based on spatiotemporal modeling of cross-scale interactions coupled with human-guided machine learning.
Catastrophes
Our goal is to improve understanding of drivers and effects of extreme events. Environmental change investigations are typically limited to observational and small-scale experimental studies, but catastrophic events offer unique multi-scale perspectives into real-life ecosystem dynamics. These sometimes surprising consequences of non-typical environmental conditions can facilitate understanding, prediction, and management for the future as our environment continues to change.
Greening of the Desert
Our goal is to improve understanding of desert landscapes under alternative rainfall scenarios in order to predict conditions that will promote grass recovery. We are integrating diverse, long-term environmental data with detailed process-based biological data and knowledge spanning multiple levels of organization obtained from disparate locations to create a “knowledge landscape map.” This map will be used to develop functional relationships to extend grass recovery predictions to other locations and time periods.