On the nose: validating a novel, non-invasive method to identify individual koalas using unique nose patterns

Abstract

Context Non-invasive identification of individual animals in wild populations can occur in species with unique coat patterns (e.g. zebras, giraffes, jaguars). However, identification in other species often relies on capture-mark–recapture techniques, involving physical handling of animals. Identification of individual koalas (Phascolarctos cinereus) is difficult and has so far relied mostly on invasive methods such as ear tagging, microchipping and/or collaring, which require capture. The validation of a non-invasive method to identify koalas could improve monitoring of individuals in the wild, allowing targeting of specific koalas in disease and survival studies, reducing the need to capture individuals. Aims This study describes a novel effective method to identify koalas from their nose markings, specifically using the unpigmented pattern of the nose to determine unique features of individuals. Methods Photographs of koalas from different populations in Victoria and New South Wales (NSW), Australia, were examined in the study. Nose patterns were traced from photographs and matched through visual assessment if they were thought to belong to the same individual. Differences in identification success between datasets from different populations and the effect of sex on match success were evaluated statistically. For the NSW koalas, the effect of lighting conditions and photographic angle were also assessed. Key results Overall identification success was 89.7% (range 87.1–91.8%) and was not affected by any of the variables tested, demonstrating that nose patterns can be used reliably to identify individual koalas. Conclusions The proposed non-invasive method is simple, yet accurate and stable over time, hence it offers a vital tool for monitoring endangered koalas whilst minimising human interference. Implications Pattern-based recognition of koalas is cost-effective, reduces stress on the animals, has the potential to improve data collection and allows involvement of citizen scientists in monitoring of populations or individuals.