Misidentification error associated with classifications of white‐tailed deer images - Chad H. Newbolt; Stephen S. Ditchkoff (Wildlife Society Bulletin)
ABSTRACT Camera traps are widely used to monitor wildlife, with important management decisions often relying on interpretation of these data. Animal misidentifications are known to be an important source of error in wildlife surveys that require the identification of unique individuals from camera‐trap data; however, the practice of broadly classifying animal images according to sex or age has received less critical attention despite the significant potential for misidentification error under certain circumstances. From 19 January to 1 April 2017, we solicited a group of 726 participants, consisting of both wildlife professionals and nonprofessionals from across the United States, to take an online survey that tested their ability to classify images of known white‐tailed deer (Odocoileus virginianus) according to sex and age. Our goal was to determine the relative influence of tested observer (i.e., experience and familiarity with classifying deer images) and image‐based factors (i.e., distance of deer from camera, day vs. night image) on accuracy of deer classifications. Our results indicated that respondents that were wildlife biologists and those with greater levels of experience viewing deer images were more accurate than others when classifying posthunting season images of deer as adult male, adult female, or fawn. However, the sex–age group of the deer was the most influential predictor of classification reliability, with branched‐antlered adult males being classified more accurately by all respondent groups than were adult females and fawns. Our findings emphasized that animal misidentifications may be an important source of survey error not only when identifying unique individuals, but also under any circumstance where comparative groups lack definitive traits. We suggest that those using camera traps to evaluate wildlife populations should select survey periods that maximize differences among classification groups, when possible, and develop species‐specific image training for observers to improve the reliability of results. Further, population demographics should be considered when evaluating the overall reliability of survey results for species where classification accuracy varies among sex–age groups. © 2019 The Wildlife Society.