title: Spotting Cheetahs: Identifying Individuals by Their Footprints.
abstract: The cheetah (Acinonyx jubatus) is Africa's most endangered large felid and listed as Vulnerable with a declining population trend by the IUCN(1). It ranges widely over sub-Saharan Africa and in parts of the Middle East. Cheetah conservationists face two major challenges, conflict with landowners over the killing of domestic livestock, and concern over range contraction. Understanding of the latter remains particularly poor(2). Namibia is believed to support the largest number of cheetahs of any range country, around 30%, but estimates range from 2,905(3) to 13,520(4). The disparity is likely a result of the different techniques used in monitoring. Current techniques, including invasive tagging with VHF or satellite/GPS collars, can be costly and unreliable. The footprint identification technique(5) is a new tool accessible to both field scientists and also citizens with smartphones, who could potentially augment data collection. The footprint identification technique analyzes digital images of footprints captured according to a standardized protocol. Images are optimized and measured in data visualization software. Measurements of distances, angles, and areas of the footprint images are analyzed using a robust cross-validated pairwise discriminant analysis based on a customized model. The final output is in the form of a Ward's cluster dendrogram. A user-friendly graphic user interface (GUI) allows the user immediate access and clear interpretation of classification results. The footprint identification technique algorithms are species specific because each species has a unique anatomy. The technique runs in a data visualization software, using its own scripting language (jsl) that can be customized for the footprint anatomy of any species. An initial classification algorithm is built from a training database of footprints from that species, collected from individuals of known identity. An algorithm derived from a cheetah of known identity is then able to classify free-ranging cheetahs of unknown identity. The footprint identification technique predicts individual cheetah identity with an accuracy of >90%.
authors.affiliation: WildTrack and Nicholas School of the Environment, Duke University.
authors.forename: Zoe C
authors.initials: ZC
authors.lastname: Jewell
authors.affiliation: WildTrack and Nicholas School of the Environment, Duke University; wildtrack.monitoring@gmail.com.
authors.forename: Sky K
authors.initials: SK
authors.lastname: Alibhai
authors.affiliation: N∕a'an ku sê Research Programme; Division of Biology and Conservation Ecology, School of Science and the Environment, Manchester Metropolitan University.
authors.forename: Florian
authors.initials: F
authors.lastname: Weise
authors.affiliation: N∕a'an ku sê Research Programme.
authors.forename: Stuart
authors.initials: S
authors.lastname: Munro
authors.affiliation: N∕a'an ku sê Foundation.
authors.forename: Marlice
authors.initials: M
authors.lastname: Van Vuuren
authors.affiliation: N∕a'an ku sê Foundation.
authors.forename: Rudie
authors.initials: R
authors.lastname: Van Vuuren
mesh_terms: D002612:Acinonyx / Q000033:anatomy & histology* / Q000502:physiology*; D000818:Animals; D005260:Female; D008297:Male; D013045:Species Specificity