2017 ESA Annual Meeting (August 6 -- 11)

COS 69-9 - Studying Serengeti wildebeest from space: A multi-scale approach for studying the behavioral ecology of group living vertebrates in the wild

Tuesday, August 8, 2017: 4:20 PM
B114, Oregon Convention Center
Lacey Hughey1, Colin J. Torney2, J. Grant C. Hopcraft3, Benezeth Mutayoba4, Dmitry Fedorov5, B.S. Manjunath5, Tom W. Bell6 and Douglas J. McCauley7, (1)Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA, (2)Mathematics, University of Glasgow, Glasgow, United Kingdom, (3)Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom, (4)Veterinary Physiology, Sokoine University of Agriculture, Morogoro, Tanzania, United Republic of, (5)Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, (6)Department of Geography, University of California, Los Angeles, (7)Ecology, Evolution, and Marine Biology, University of California at Santa Barbara, Santa Barbara, CA
Background/Question/Methods

A major limitation to our capacity to intelligently manage planetary biodiversity has been the necessity to make large-scale decisions based on data gathered from very few well-studied individuals. Such is the case in the Greater Serengeti Ecosystem (GSE), where our understanding of the migration of more than 1 million wildebeest is based on the movement dynamics of a handful of GPS collared individuals. The inability to synchronously measure the movement and behavior of a large proportion of the population hampers our understanding of this species and its relationship to the ecosystem in which it is embedded. However, recent advances in very high resolution satellite imagery now provide a powerful opportunity to quantitatively study the behavior of entire herds and even entire populations at a spatial scale that was impossible historically. We used field observations of the GSE wildebeest population to identify behaviors associated with stable state herding geometries and demonstrate how satellite images may be used to classify and map the distribution of animal behaviors across multiple spatial scales.

Results/Conclusions

We classified and quantitatively described the following stable state herding geometries and associated behaviors: line (transit), wave front (mobile feeding), dispersed (stationary feeding), vacuole (vigilant). In addition to demonstrating the ability to use these geometries to infer group behavior from satellite images, we highlight intriguing similarities between the structure and function of these geometries and those observed in smaller bodied organisms (e.g., fish schools and insect swarms).