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AUTHORS: Jessica McDaniel, School of Natural Resources, University of Nebraska - Lincoln; Andrei Snyman, Department of Biological Sciences, California State University - Los Angeles; John P. Carroll, School of Natural Resources, University of Nebraska - Lincoln
ABSTRACT: The African Civet (Civettictis civetta) is a secretive, nocturnal mammal distributed over a broad part of the African continent. This species has been widely utilized for musk production, which has led to declining populations. As a result, Botswana populations are listed under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix III. In order to provide effective conservation, status assessment measures of abundance are necessary. Camera trapping is now widely used to detect nocturnal and secretive species. We believe that the unique spot patterns found on civets provides opportunity to develop individual identification for application in CMR abundance estimates. We are using photographs from 6 years of camera trapping studies to create a database of civet photos. These photos are processed through Automated Photo-identification Software (APHIS) using two techniques. Spot Pattern Matching (SPM) uses referenced points to create spot coordinates to form a fingerprint-like statistic in the matching portion of the pre-processing stage. The SPM method comes from an I3S software that is implemented within APHIS. Image Template Matching (ITM), a method specific to only APHIS, uses patterns in pixels to determine a match through a pre-processing stage. Using both methods, we were able to match these patterns on African civets because it allowed for the full mid-body of the civet and all of its characteristic to be accounted for, thus increasing the likelihood of finding the correct match. We are testing individual recognition efficacy indirectly using the multiple photo settings on the camera traps creating multiple images of known animals. We believe that the combination of camera trapping and individual identification of civets will allow streamlined and cost effective analyses of distribution and abundance.
