Data Science in Biology

Environmental Data Science

Data science plays a vital role in advancing the fields of Ecology, Epidemiology, and Genomics. By employing sophisticated demographic and disease simulations, we can make accurate predictions about populations and their pathogens. The management, access, and analysis of vast amounts of data, including genomic data, necessitate robust programming and data science techniques.

Data Science for Ecology

We have extensive expertise in developing ecological models for populations, metapopulations, and metacommunities. These sophisticated analyses enable us to predict population dynamics.

• 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 observed patterns from historical disease surveillance data, we employ data science techniques to predict future transmission patterns and conduct risk assessments.

• 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

We leverage high-performance computing to study the genomes of wildlife and plants. This approach allows us to gain valuable insights into species evolution and develop effective population management strategies.

• 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