I received my PhD from North Carolina State University in 2011 studying the biogeography of carnivores and their parasites. From 2012-2013, I was a NSF and Chancellor’s postdoctoral fellow in the Environmental Science, Policy and Management Department at University of California, Berkeley. Most recently, I completed a postdoctoral fellowship at the Luc Hoffmann Institute, WWF International (2014-2015).
The use of remote cameras to study animal populations remains a growing field in ecology. In the Applied Wildlife Ecology (AWE) lab, we deploy cameras across vast geographic scales in Michigan and multiple countries in West Africa to understand the distribution and activity patterns of mammalian carnivores particularly those of conservation concern such as gray wolves and lions, respectively. However, after we complete the laborious field efforts associated with the camera survey then emerges the new challenge of processing millions of the resulting images. We have to manually sort images that have no animals, non-target animal species, or target species. To assist, we created Michigan ZoomIN, a virtual citizen science project to crowdsource identification of images obtained throughout the state. Additional questions now require thoughtful assessment to ensure data accuracy, promote sustained public engagement, and differentiate individuals of the target species. For example, through collaboration, we could study the users of the site by creating profiles of their activity both from identifications and posting on the talk board as well as increase weighting their classifications in a consensus algorithm based on accuracy scores. Other considerations we'd still like to explore include machine learning to find target species only or more simply remove "empty" images, and differentiate individuals of the same species through specific morphometric calculations. Recent advancement in technology can allow for revolutionary transformation for the study of wildlife and enhance impact of such scholarship for conservation and human populations alike.