New tech could help keep better track of Serengeti wildbeest

New methods of counting wildlife could provide conservationists with fast and accurate methods for estimating the abundance of natural populations. In a new paper published in the journal Methods in Ecology and Evolution , mathematicians and conservationists from the UK, Africa and the United States discuss how they have used both machine-learning and citizen science techniques to accurately count wildebeest in the Serengeti National Park in Tanzania more rapidly than is possible using traditional methods. Evaluating wildebeest abundance is currently extremely costly and time-intensive, requiring manual counts of animals in thousands of aerial photographs of their habitats. From those counts, which can take months to complete, wildlife researchers use statistical estimates to determine the size of the population. Detecting changes in the population helps wildlife managers make more informed decisions about how best to keep herds healthy and sustainable. The team which produced the paper was comprised of researchers from the University of Glasgow, the University of Cape Town in South Africa, the Field Museum of Natural History in the USA and the Tanzania Wildlife Research Institute (TAWIRI). They used a deep-learning algorithm to identify the wildebeest in images taken from the 2015 aerial survey of the Serengeti National Park.