Likelihood-Based Photograph Identification: Application With Photographs of Free-Ranging Bison

Using photographs to identify individual animals and monitor populations is becoming more common. However, photographic identification methods where measurements of morphological traits (e.g., horn length) are compared have received little attention. We present an approach for aiding with the identification of individual animals from photographs. The approach incorporates measurement data, metadata from photographs, and multiple sources of error, and calculates a matching score between pairs of photographs using a likelihood-based algorithm. We tested and identified the false-rejection error rate using 91 photographs, representing 33 known free-ranging bison (Bison bison), and 117 simulated data sets with varying numbers of unique individuals, morphological measurements, and photograph error. We then used the approach to estimate the adult population size of bison in Prince Albert National Park, Canada, in 2011. For bison, the false-rejection rate of our approach was 0.055. Using a Huggins closed population model with misidentification, we estimated 103 (95% CI = 82-130) and 46 (95% CI = 37-58) adult female and male bison, respectively. After incorporating field-based calf- and juvenile-to-female ratios, we estimated 202 (95% CI = 171.6-231.4) bison. We found this estimate to be plausible using 2 minimum-count aerial surveys conducted in March 2011 and 2012 for comparison. With our approach, researchers and managers can build capture histories of individuals, which can be used for studies of population dynamics and habitat selection. This approach can incorporate any morphological measurements extracted from photographs (e.g., coat color), making it robust to a variety of species and study systems. (C) 2013 The Wildlife Society.