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

An objective, non-invasive technique was developed for identifying individu al black rhino from their footprints (spoor). Digital images were taken of left hind spoor from tracks (spoor pathways) of 15 known black rhino in Hwa nge 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 d iscriminant and canonical analyses. Discriminant analysis of spoor measurem ents from all 15 known animals, employing the 30 measurements with the high est F-ratio values, gave very close agreement between assigned and predicte d classification of spoor. For individual spoor, the accuracy of being assi gned to the correct group varied from 87% to 95%. For individual tracks, th e accuracy level was 88%. Canonical analyses were based on the centroid plo t method, which does not require pre-assigned grouping of spoor or tracks. The first two canonical variables were used to generate a centroid plot wit h 95% confidence ellipses in the test space. The presence or absence of ove rlap between the ellipses of track pairs allowed the classification of the tracks. Using a new 'reference centroid value' technique, the level of accu racy was high (94%) when individual tracks were compared against whole sets (total number of spoor for each rhino) but low (35%) when tracks were comp ared against each other. Since tracks with fewer spoor were more likely to be misclassified, track sizes were then artificially increased by summing s maller tracks for the same rhino. The modified tracks in a pairwise compari son gave an accuracy of 93%. The advantages, limitations and practical appl ications of the spoor identification technique are discussed in relation to censusing and monitoring black rhino populations.