Movement data have become essential for our understanding of animal ecology. However, such data are associated with a broad range of challenges. For example, tracking the movement of many species (e.g. fish and small birds) is still limited to inaccurate technology, such as light-based geolocation. Making behavioral and spatial inferences based on such data is difficult because the tracks they create are not good representations of the animals’ movement. Understanding the behavior of animals using movement data is challenging even with accurate movement data, because we are often making inference on a process that is not observed directly. Through a set of examples from a broad range of animals (fish, bears, marine mammals), I will demonstrate how a state-space modeling framework can improve our understanding of animal movement.
Dr. Auger-Méthé is an assistant professor in the Department of Statistics and in the Institute for the Oceans & Fisheries, and is a Canadian Research Chair in Statistical Ecology. Most of her work is interdisciplinary and at the intersection between ecology, statistics, and marine sciences. Her recent focus has been on developing and applying statistical models to understand the movement and space use of marine species. Prior to starting at UBC, Dr. Auger-Méthé did her PhD at the University of Alberta, and a BSC, MSc, and Postdoctoral fellowship at Dalhousie University.