Understanding East African Wildlife Through Big Data and Bio-Tagging
Over the last fifty years the grazing spaces available for wildlife and livestock have shrunk due to changes in climate and politics. We propose to develop a a novel MEMS sensor package to better understand wildlife and livestock interactions in the semi-arid ecosystems of East Africa where wildlife and livestock are sympatric. Using this sensor package we propose to work with local Maasai pastoralists in Kenya to tag cattle and ungulate wildlife species at fine scales of space and time. We hope to use this technology to determine the: (1) extent of overlap; (2) feeding preferences, and; (3) browsing rates, between wildlife and livestock. This project could have transformative impacts for the scientific understanding of livestock grazing along with the associated social dynamics with actionable outcomes for the management of wildlife conservancies.