Understanding wildlife connectivity and disease spread through GPS tracking

Movement is a fundamental process driving population connectivity as well as the spread of infectious disease and invasive species.  Recent advances in technology have made it possible to track animals at extremely high resolution. Hanks will show tracking data from a wide range of systems, including insects, birds, and mammals, and illustrate approaches for analyzing tracking data using modern statistical and machine learning tools. These analyses will focus on examining how individual animal movements can be explained and predicted using remotely sensed data and how individual movements scale up and define population-level structure, connectivity, and fitness. Ephraim Hanks is a statistician with a specialty in methods for spatial and spatio-temporal data. Hanks’ research focuses on advances in statistical modeling and computation to increase the ability to ask and answer scientific questions in ecology, epidemiology and other fields. His work is motivated by collaborations with ecologists and epidemiologists.