NSF: Unifying mathematical and statistical approaches for modeling animal movement and resource selection
September 2016 - July 2020
- National Science Foundation
Modeling animal movement through landscapes is a key component to understanding population
ecology, how populations can be managed, how human actions impact the population, and how the population could respond to anthropogenic change factors such as climate and/or urbanization. Dramatic improvements in two critical types of data have recently occurred: remotely sensed environmental data and high-resolution animal location (telemetry) data. These data drive a statistical industry serving Federal and State wildlife management agencies, private companies, and academia. This is a collaborative project funded by the National Science Foundation, and in conjunction with Colorado State University, and South Dakota School of Mines and Technology. We will combine approaches in statistics and mechanistic dispersal models and develop a unified method for analyzing telemetry data that naturally accommodates heterogeneous environments and barriers/constraints to movement. Developed tools will be applied to foraging ungulates in Utah, harbor seals in the Gulf of Alaska, and Canada lynx in Colorado.