ESR 4:205-218 (2008)  -  DOI: https://doi.org/10.3354/esr00067

ABSTRACT: A non-invasive and cost-effective footprint identification technique (FIT) is presented, which can aid the identification of individual white rhino Ceratotherium simum and the differentiation of this species from black rhino Diceros bicornis. FIT is an adaptation of a traditional tracking identification technique and is a useful censusing and monitoring tool for wildlife conservation. We implemented FIT to identify 40 white rhino. Geometric profiles were extracted from digital images of footprints, and subjected to an algorithm based on multivariate statistical analyses. FIT’s classification rules were tested using a dataset of 1276 footprints from 159 tracks of 40 white rhino from a fenced wild population in Namibia. Using 2 different test models for pairwise track matching, FIT gave accuracies of 91 and 95% for population estimate (census) prediction. In a monitoring scenario (matching a ‘test’ track to one of the footprint sets of known individuals) the accuracies for 2 test models were 97 and 99%. For species discrimination, we used a dataset of 1636 footprints, with 218 tracks of which 59 were from black and 159 from white rhino. FIT gave a species discrimination accuracy for tracks of 98 to 99% using 3 different test models. We outline how the underlying FIT has been adapted for white rhino and detail work in progress to extend the method to other species. We anticipate that the technique will offer an objective and accurate tool for monitoring and censusing, with flexibility as regards target species and locale. Data collection for FIT is intuitive for skilled trackers and thus local expertise can be employed. The technique promises to be an effective tool for management and ecological studies, especially for nocturnal or otherwise elusive species, and is expected to be effective as a complementary tool for other monitoring techniques, such as mark-recapture or camera-trapping.

KEY WORDS: Footprint · Non-invasive · Monitoring · Censusing · White rhino · Tracking