Movement Ecology

Movement Ecology IconMovement ecology is the study of how organisms – either animals, plants or microorganisms – move within their environments or ecosystems, such as during daily activities like foraging, or when dispersing across the landscape, or during annual migrations.

Associated Faculty

Sonia Altizer
Sonia Altizer

Martha Odum Distinguished Professor of Ecology
Director of Public Service and Outreach
Graduate Program Faculty

Ecology building, Rm. 190
Office: (706) 542-9251
Fax: (706) 542-4819

Andy Davis
Andy Davis

Assistant Research Scientist
Graduate Program Faculty

Biological Sciences Bldg., Room 714
Office: (706) 542-8112
Fax: (706) 542-4819

Richard Hall
Richard Hall

Associate Professor
Graduate Program Faculty
Joint appointment: Dept. of Infectious Diseases, College of Veterinary Medicine

Ecology building, Rm. 137

Ricardo Holdo
Ricardo Holdo

Professor
Graduate Program Faculty

Ecology building, Rm. 188-B
Office: (706) 542-0075
Fax: (706) 542-3344

Saran Traoré
Saran Traoré

Visiting Professor
Fulbright Scholar

Upcoming Events

Ecology Seminar Series: Kevin Vogel

10:20 am

Ecology Building Ecology Auditorium (Room 201)

Jasmine Longmire, Ecology Thesis Defense Seminar

10:00 am

Ecology 111, CEID Conference Room

Ecology Seminar Series: Joe Reustle

10:20 am

Ecology Building Ecology Auditorium (Room 201)

Ecology Seminar Series: Raul Costa-Pereira

10:20 am

Ecology Building Ecology Auditorium (Room 201)

Latest News

The monarch butterfly may not be endangered, but its migration is

Breeding population of monarchs is stable, but they’re dying off on their way to Mexico With vigorous debate surrounding the health of the monarch butterfly, new research from the University

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1999 — The Visionary: Beth Shapiro

Ecology alumna Beth Shapiro (BS/MS ’99), author of the 2015 book How to clone a mammoth: The science of de-extinction, is profiled in an alumni spotlight.

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Machine learning may lead to better flu vaccines

Alpha Forna, a postdoc in the Drake and Rohani labs, developed a system that uses machine learning to predict how a seasonal flu virus is expected to evolve—with nearly 73% accuracy.

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