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.
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 classi®cation 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 ®rst two canonical variables were used to generate a centroid plot with 95% con®dence ellipses in the test space. The presence or absence of overlap between the ellipses of track pairs allowed the classi®cation 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 misclassi®ed, track sizes were then arti®cially increased by summing smaller tracks for the same rhino. The modi®ed tracks in a pairwise comparison gave an accuracy of 93%. The advantages, limitations and practical applications of the spoor identi®cation technique are discussed in relation to censusing and monitoring black rhino populations.
Summary1. Most hypotheses for translocation success are elaborate, hierarchical, and untested combinations of socio-ecological predictors. Empirical support for those tested is vulnerable to spurious single-predictor relationships and does not account for the hierarchy amongst predictors and nonindependence amongst individuals or cohorts. Testing hypotheses as a priori multi-level models promotes stronger inference. 2. We apply a 25-year (1981-2005) data base including 89 reintroduction and 102 restocking events that released 682 black rhinoceros Diceros bicornis into 81 reserves to test 24 hypotheses for translocation success, defined as survival to 1 year post-release. We made information-theoretic comparisons of hypotheses represented as hierarchical models incorporating random effects for reserve and release cohort predictors of death. 3. Mortality rates after restocking were higher than for reintroductions (13AE4 cf. 7AE9%, respectively) due largely to intraspecific fighting. No predictors strongly influenced reintroduction success, although cohorts consisting entirely of adult males were 8AE2% of individuals but contributed 21AE9% of deaths, and reserves with lowest carrying capacities (i.e. <0AE1 rhino km )2 ) had a 16AE3% mortality rate. Most models for restocking success were not supported. Only those including age class received substantial support. Age was the only predictor to strongly influence death rates. Predictors previously thought influential, like population density, reserve area and quality, and cohort size, were not supported. 4. Synthesis and applications. Simple rules succeeded where complex ecological and demographic hypotheses failed to predict survival after translocation of critically endangered black rhinoceros. Results support bold attempts by managers at translocations towards species recovery in most ways that they have historically occurred. Groups of rhinoceros of different size and composition can be successfully moved over large distances between different ecological contexts. Also, the release of cohorts into reserves that are relatively small, poorer habitat or already stocked need not be avoided so long as calves and all-male cohorts are not reintroduced, and only adults used for restocking. Our analysis demonstrates the importance of information-theoretic comparisons of a priori hierarchical models to test hypotheses for conservation management. We caution against interpreting simple correlations or regression amongst a large number of nested ecological and demographic variables.
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