FIT (Footprint Identification Technology) in JMP: Monitoring Giant Panda Populations
Sky Alibhai, Director and Co-Founder, WildTrack
Zoe Jewell, President and Co-Founder, WildTrack
Li Binbin, Assistant Professor of Environmental Science, Environmental Research Center, Duke Kunshan University
The world's most iconic species needs help, research shows. To help this endangered species, conservationists need reliable population and distribution data. This is where the scientists at WildTrack and Duke Kunshan University come in. The scientists worked hand in hand with Chinese government researchers to collect giant panda footprint data using a cost-effective, non-invasive, nature-friendly track recognition technique. Subsequently, WildTrack built highly accurate algorithms to classify individuals and determine sex, which is currently the most successful method of giant panda data collection and analysis. Methods for animal conservation have been evolving, with the deployment of high-resolution drones that can capture footprints in previously inaccessible areas, and exploring how deep learning can be applied to help filter ever-increasing amounts of data. This work helps create a better future for wild giant panda populations and helps protect other species in the same habitats that are among the most biodiverse on Earth.
Zoe Jewell
President and co-founder of WildTrack, Zoe Jewell holds a BSc in Zoology/Physiology and an MSc in Medical Parasitology from the London School of Tropical Medicine and Hygiene, London University and a Veterinary Medicine degree from Cambridge University. Jewell not only works at Animal Consultants International, responsible for wildlife monitoring and rescue, but also joins the Society for Conservation Biology. She has published on the use of non-invasive methods to monitor animals and the ethics of animal monitoring. She is currently a Visiting Professor in the Department of Mechanical and Aerospace Engineering at North Carolina State University, a Visiting Associate Professor in the School of the Environment and Earth at Duke University, and an Associate Professor at the University of Technology in Sydney. Member Scholars of the Center for the Preservation of Charities.
Sky Alibhai
Director and co-founder of WildTrack, Sky Alibhai holds a BSc in Zoology from Makerere University, Uganda, and a PhD in Small Mammal Population Dynamics from the University of Oxford. He is a fellow of the Zoological Society of London and a member of the International Society for Conservation Biology. Alibhai has published extensively on using non-invasive methods to monitor wildlife. He is currently a visiting professor at North Carolina State University's Department of Mechanical and Aerospace Engineering, a visiting associate professor at Duke University's School of the Environment and Earth, and a member scholar at the Center for Conservation Philanthropy at the University of Technology, Sydney.
Photos on this page: http://wildtrack.org/about-us/who-we-are/
Li Binbin
Li Binbin is an assistant professor of environmental science at Duke Kunshan University's Environmental Research Center and an adjunct faculty member at Duke University's School of the Environment and Earth. Her research focuses on biodiversity decline, conservation of endangered and endemic species (such as giant pandas), prioritization and management of protected areas, and advocacy for innovative technologies, markets, and policies that address conservation issues and advance local nature. Binbin holds a Ph.D. in Environment from Duke University (2017), a Master of Science in Natural Resources and the Environment from the University of Michigan (2012), and a double Bachelor's degree in Life Sciences and Economics from Peking University (2010). As a science communicator and nature educator, Binbin works as a nature photographer in the mountains of southwest China, and is also a member of the advisory board of Disney's nature documentary "Born in China".
Forecasting and Encapsulation
John Sall, Co-Founder and Executive Vice President, SAS
As the number of tables increases, the amount of data expands, and the functions to be encapsulated by the model continue to increase, we hope to make further improvements in organization, containment, and modeling. The release of JMP 14 will open up new ways to address these needs.
