Sky K. Alibhai

Emerging Technologies to Conserve Biodiversity

Technologies to identify individual animals, follow their movements, identify and locate animal and plant species, and assess the status of their habitats remotely have become better, faster, and cheaper as threats to the survival of species are increasing. New technologies alone do not save species, and new data create new problems. For example, improving technologies alone cannot prevent poaching: solutions require providing appropriate tools to the right people. Habitat loss is another driver: the challenge here is to connect existing sophisticated remote sensing with species occurrence data to predict where species remain. Other challenges include assembling a wider public to crowdsource data, managing the massive quantities of data generated, and developing solutions to rapidly emerging threats.

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

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.

Identifying endangered species from footprints

To protect endangered species and understand extinction threats, we need effectivemonitoring techniques. Biologists have documented around one million species, which represent only 1–10% of all those on earth. Less quantified, however, is how human activity elevates local extinction rates, particularly in vulnerable endemic populations.1 Furthermore, existing methods for monitoring are variable in effectiveness. One approach is to attach telemetry devices to wildlife,2 but this can have negative effects on the animal, inducing pathological stress,3 altered behavior,4 and reduced female fertility.5 Such invasive approaches are also costly and rarely involve local communities, whose commitment to the conservation project is essential. We need low-cost, non-invasive, and community-friendly monitoring techniques for sustainable and effective conservation, without negative impacts. Technology is providing new methods for wildlife observation. The use of remote cameras,6 tracking patterns in vocalization7 and coat,8 and analyzing DNA from feces and hair9 are all valuable in identifying individuals but can have a limited range of application, low accuracy, and high cost. Footprints, by contrast, are ubiquitous on suitable substrates, cheap to collect, and can provide good biometric markers. Some scientists have identified small numbers of captive individuals from footprints, but have had difficulty scaling up the work for larger numbers, in classifying at different levels, and in applying the technique to wild populations.10–12 We worked for many years with expert trackers in Africa and observed their accuracy in identifying individuals from footprints along trails in the bush. To translate their techniques for modern technology we needed a robust analytical tool, capable of effective discrimination on the basis of species, individual, sex, and age-class. There are several levels of complexity in footprint identification. Every species has a unique footprint anatomy, and every individual of a species a unique footprint. Figure 1. Left hind tiger footprints in different substrates, which are ubiquitous and provide good biometric markers. (Photo courtesy of WildTrack.)

Spotting Cheetahs: Identifying Individuals by Their Footprints

The cheetah (Acinonyx jubatus) is Africas most endangered large felid and listed as Vulnerable with a declining population trend by the IUCN1. It ranges widely over sub-Saharan Africa and in parts of the Middle East. Cheetah conservationists face two major challenges, conflict with landowners over the killing of domestic livestock, and concern over range contraction. Understanding of the latter remains particularly poor2. Namibia is believed to support the largest number of cheetahs of any range country, around 30%, but estimates range from 2,9053 to 13,5204. The disparity is likely a result of the different techniques used in monitoring. Current techniques, including invasive tagging with VHF or satellite/GPS collars, can be costly and unreliable. The footprint identification technique5 is a new tool accessible to both field scientists and also citizens with smartphones, who could potentially augment data collection. The footprint identification technique analyzes digital images of footprints captured according to a standardized protocol. Images are optimized and measured in data visualization software. Measurements of distances, angles, and areas of the footprint images are analyzed using a robust cross-validated pairwise discriminant analysis based on a customized model. The final output is in the form of a Wards cluster dendrogram. A user-friendly graphic user interface (GUI) allows the user immediate access and clear interpretation of classification results. The footprint identification technique algorithms are species specific because each species has a unique anatomy. The technique runs in a data visualization software, using its own scripting language (jsl) that can be customized for the footprint anatomy of any species. An initial classification algorithm is built from a training database of footprints from that species, collected from individuals of known identity. An algorithm derived from a cheetah of known identity is then able to classify free-ranging cheetahs of unknown identity. The footprint identification technique predicts individual cheetah identity with an accuracy of >90%.

The challenge of monitoring elusive large carnivores: An accurate and cost-effective tool to identify and sex pumas (Puma concolor) from footprints

Acquiring reliable data on large felid populations is crucial for effective conservation and management. However, large felids, typically solitary, elusive and nocturnal, are difficult to survey. Tagging and following individuals with VHF or GPS technology is the standard approach, but costs are high and these methodologies can compromise animal welfare. Such limitations can restrict the use of these techniques at population or landscape levels. In this paper we describe a robust technique to identify and sex individual pumas from footprints. We used a standardized image collection protocol to collect a reference database of 535 footprints from 35 captive pumas over 10 facilities; 19 females (300 footprints) and 16 males (235 footprints), ranging in age from 1–20 yrs. Images were processed in JMP data visualization software, generating one hundred and twenty three measurements from each footprint. Data were analyzed using a customized model based on a pairwise trail comparison using robust cross-validated discriminant analysis with a Ward’s clustering method. Classification accuracy was consistently > 90% for individuals, and for the correct classification of footprints within trails, and > 99% for sex classification. The technique has the potential to greatly augment the methods available for studying puma and other elusive felids, and is amenable to both citizen-science and opportunistic/local community data collection efforts, particularly as the data collection protocol is inexpensive and intuitive.