Photographic Identification of Individual Domestic Cats: Comparing the Accuracy of Life Science University Students Versus Cat Advocate Citizen Science Volunteers

Abstract

There have been no long-term field studies of the potential effect of spay/neuter programs on free-roaming domestic cat population sizes. To address that gap via citizen science, we are developing a novel approach to photographic mark-recapture population research that engages volunteers as both smartphone-wielding data collectors and as online data processors in building capture histories from submitted photos. Here, we present a validation study testing the accuracy of cat advocate volunteers at matching smartphone photos of cats, and we compare their success to a reference group of life science university students. We also examine feline photographic identification from two additional perspectives: what makes a volunteer better at cat identification, and what makes a cat photo more identifiable? 151 cat advocates and 17 students completed 37,800 pairwise photo comparisons using our online platform. Cat advocates’ matching attempts (n = 34,080) were correct 98.1% of the time compared with students’ 97.5% (n = 3,720). Volunteers who reported a pet cat increased their accuracy. Volunteers who held less than a bachelor’s degree, or those who volunteered with cats previously, had reduced accuracy. If a cat was a color other than black, its ability to be identified increased. We demonstrated that our citizen science volunteer sample was not only adequate at identifying individual cats in smartphone photos, but performed better than our sample of life science students—a labor pool commonly trusted to organize data from camera trap research. While photographs are the data foundation of many studies of free-roaming cats, we are the first to analyze by-eye visual identification in this species.