I am interested in how the behavior of animals drives ecological processes across scales, from groups to populations, communities, and ecosystems. I use collective behavior, anti-predator behavior, and movement ecology as lenses to explore these cross-scale behavioral dynamics, and work with African ungulates as my main study system. The ultimate goal of my work is to develop a mechanistic understanding of the behavioral drivers of ecological processes in order to predict how complex ecological systems will respond to rapid environmental change.

Studying dynamic, multi-scale behavioral processes requires high-resolution data on many interacting individuals, along with information on their physical and social environments. To meet these data demands, I have developed drone- and image-based methods for quantitative field studies of animal behavior, and collaborate closely with computer scientists and engineers to develop new robotics and AI technologies for applications in wildlife ecology and conservation.

Antipredator behavior

Predation has a strong negative impact on prey fitness, and thus drives many interesting behavioral adaptations by which prey avoid, detect, or escape their predators. These behavior also affect how animals interact with their social, biotic, and abiotic environments, and thus potentially have knock-on effects at broader ecological scales.

My research addresses the individual and collective antipredator behaviors of African ungulates, with a particular focus on vigilance and detection of predators, and the broader ecological impacts of these behavioral strategies.

Movement ecology

Animal movement is a fundamental behavior that mediates many key ecological processes. The proliferation of on-animal sensors (“biologgers”) has led to a surge in research in movement ecology. However, biologging approaches are limited in their ability to capture the social and environmental context in which movement occurs. Thus, our interpretations of movement data are based on assumptions regarding the behavior and decisions that underly movement patterns.

My group uses drones to collect high-resolution image data to examine these assumptions and explore the drivers of individual and collective movement patterns across spatial scales.

AI for wildlife ecology and conservation

Ecology is rapidly developing into a data-rich discipline. This transition is driven by advances in sensor technologies that allow us to capture data from the environment at unprecedented scales, and the concurrent development of quantitative methods for processing, interpreting and analyzing these datasets. Meanwhile, we are also in the midst of a global extinction crisis driven by climate change and other anthropogenic factors. There is thus both an urgent need to improve our ecological understanding in order to predict how species will respond to rapid environmental change, and an opportunity to harness sensors and data-driven approaches to meet this need.

I bridge the gap between fields by collaborating with computer scientists and engineers to develop hardware and software systems that enable new ecological science, and by applying these technologies to studies of animal behavioral ecology that integrate behavioral insights across ecological scales.