Censusing and monitoring black rhino (Diceros bicornis) using an objective spoor (footprint) identification technique

Abstract

An objective, non-invasive technique was developed for identifying individual black rhino from their footprints (spoor). Digital images were taken of left hind spoor from tracks (spoor pathways) of 15 known black rhino in Hwange National Park, Zimbabwe. Thirteen landmark points were manually placed on the spoor image and from them, using customized software, a total of 77 measurements (lengths and angles) were generated. These were subjected to discriminant and canonical analyses. Discriminant analysis of spoor measurements from all 15 known animals, employing the 30 measurements with the highest F-ratio values, gave very close agreement between assigned and predicted classification of spoor. For individual spoor, the accuracy of being assigned to the correct group varied from 87% to 95%. For individual tracks, the accuracy level was 88%. Canonical analyses were based on the centroid plot method, which does not require pre-assigned grouping of spoor or tracks. The first two canonical variables were used to generate a centroid plot with 95% confidence ellipses in the test space. The presence or absence of overlap between the ellipses of track pairs allowed the classification of the tracks. Using a new ‘reference centroid value’ technique, the level of accuracy was high (94%) when individual tracks were compared against whole sets (total number of spoor for each rhino) but low (35%) when tracks were compared against each other. Since tracks with fewer spoor were more likely to be misclassified, track sizes were then artificially increased by summing smaller tracks for the same rhino. The modified tracks in a pairwise comparison gave an accuracy of 93%. The advantages, limitations and practical applications of the spoor identification technique are discussed in relation to censusing and monitoring black rhino populations.