| Summary: | Specialization: Wildlife Ecology and Management Degree: Master of Science Abstract: Estimating animal abundance is a key component of wildlife management and mark-recapture surveys are one of the most commonly used methods of obtaining population estimates. Photographic identification has recently been explored as a method of ‘marking’ individuals for mark-recapture surveys. It is most often used on species with spots, stripes of other unique markings however, in a few cases, species that do not have such obvious marks have been successfully identified using morphological measurements. There is a need for the development of non-invasive, affordable and accurate methods of censusing wide-ranging and elusive northern populations of ungulates. In this study, I tested a likelihood-based photographic identification method on 4 species of ungulates: muskox (Ovibos moschatos), Dall sheep (Ovis dalli), mountain goats (Oreamnos americanos) and mule deer (Odocoileus hemionus). False-rejection (FRR) and false-acceptance (FAR) error rates were identified for each species. These measures varied widely among species (i.e., FRR = 0–13%, FAR = 2–22%). Matching success was also determined for each species and ranged from 48% to 96%. Muskox (FRR=0% and FAR =3%) and sheep (FRR=4% and FAR=2%) had the lowest misidentification rates and the highest matching success rates (96% and 88%, respectively). Moderate results were obtained for goats (FRR=11% and FAR=6%) and deer (FRR=11% and FAR=0%) with matching success rates of 81% and 80%, respectively. An automated matching success rate was calculated based on the top-ranked photograph for each potential match and was compared to the observer matching success rate. The observer matching success rate was significantly higher for all analyses (t9=7.2, p<0.05), indicating that the final subjective user choice step of the method was important. An observer bias test was conducted for deer and sheep and significant observer bias was found for deer, and affected matching success rates for both ...
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