Statistical modeling

In collaboration with the National Phenology Network, we are creating an analytical pipeline to estimate spatial trends in the time to reach different thresholds of growing degree days in the Northeastern USA.

We are working with David More and Yang Li of the School of Natural Resources & the Environment to build an analytical pipeline characterizing uncertainty in estimates of above-ground biomass, to help stakeholders make informed decisions about which data products to use for their needs.

We developed a multivariate hierarchical Bayesian regression model that estimates trends by state, handles missing data, and predicts future growth of organic farming in the US from USDA survey data.

Automated categorization of rangeland science publications using natural language processing to help rangeland managers find the information they want.

As part of the SENTINEL: SENsing Threats In Natural Environments project, our group conducted biophysical and ecological modeling to predict the functioning, ecological impact, and physiology of plants that have been genetically modified to act as sensors to detect chemical and biological threats.

We designed hierarchical Bayesian analysis to facilitate a paper on postfire effects of rangeland restoration treatments.