William F. Fagan (University of Maryland, USA)
Biography
Bill Fagan is a Distinguished University Professor in the Biology Department at the University of Maryland. He received an Honors B.A. from the University of Delaware (1992), a Ph.D. in Zoology from the University of Washington (1996), and was a postdoctoral fellow at the National Center for Ecological Analysis and Synthesis. His research, which emphasizes the interplay between data and theory, sits at the interface of mathematics and biology, where he has worked on a wide range of topics with many collaborators from diverse fields.
He is an elected Fellow of both the Ecological Society of America and the American Association for the Advancement of Science, and he also received a Guggenheim Fellowship and the Presidential Award of the American Society of Naturalists. Over his career, he has worked on a variety of projects in spatial ecology and quantitative conservation biology.
He has authored over 285 journal articles which have collectively garnered ~38,000 citations, and has twice had the cover of Science magazine for his research on animal movement ecology.
Currently, his externally funded research focuses on mathematical investigations of migration and other long-distance animal movements and the spread of disease. These projects have taken him around the world, including research on the steppes of Mongolia (studying the movement ecology of gazelles), the seasonally flooded Pantanal grasslands of Brazil (modeling the spatial ecology of armadillos and giant otters), and the icy coasts of Antarctica (studying the spatial distribution and population dynamics of penguins).
Learning and memory in models of animal movement
Explosive growth in the availability of animal movement tracking data is providing unprecedented opportunities for investigating the linkages between behavior and ecology over large spatial scales. Cognitive movement ecology brings together aspects of animal cognition (perception, learning, and memory) to understand how animals’ context and experience influence movement and space use, affording insights into encounters, territoriality, migration, and biogeography, among many other topics. Such datasets provide a rich source of inspiration for mathematical modeling. Here I will discuss several recent and ongoing models concerning the ways in which different kinds of learning and memory shape spatial dynamics with specific attention to movement paths, migration, and consumer-resource matching.
