Data Science in Biology

Environmental Data Science

We develop reproducible analysis pipelines across ecology, epidemiology, and genomics so teams can make policy and management decisions with confidence.

Data Science for Ecology

Our ecology models support population forecasting, scenario testing, and management evaluation for complex environmental systems.

• Anthropogenic factors predict movement of an invasive species
• Machine learning to classify animal species in camera trap images: Applications in ecology
• Automated classification of bat echolocation call recordings with artificial intelligence
• Propagule size and structure, life history, and environmental conditions affect establishment success of an invasive species

Epidemiology with Data Science

Using surveillance and observational data, we estimate transmission risk and forecast likely outbreak pathways under alternative interventions.

• Detection error influences both temporal seroprevalence predictions and risk factors associations in wildlife disease models
• Accounting for heterogeneous invasion rates reveals management impacts on the spatial expansion of an invasive species

Genomics with Data Science

Our genomics workflows use scalable computing to support species monitoring, evolutionary inference, and applied conservation planning.

• Highly variable reproductive isolation among pairs of Catostomus species
• Analysis of Population Genomic Data from Hybrid Zones
• Speciation, species persistence and the goals of studying genomic barriers to gene flow
• Mixed ancestry from wild and domestic lineages contributes to the rapid expansion of invasive feral swine