Integrated modeling of bilateral photo-identification data in mark-recapture analyses.

When natural marks provide sufficient resolution to identify individual animals, noninvasive sampling using cameras has a number of distinct advantages relative to "traditional" mark-recapture methods. However, analyses from photo-identification records often pose additional challenges. For example, it is often unclear how to link left- and right-side photos to the same individual, and previous studies have primarily used data from just one side for statistical inference. Here we describe how a recently developed statistical method can be adapted for integrated mark-recapture analyses using bilateral photo-identification records. The approach works by assuming that the true encounter history for each animal is a latent (unobserved) realization from a multinomial distribution. Based on the type of photo encounter (e.g., right, left, or both sides), the recorded (observed) encounter histories can only arise from certain combinations of these latent histories. In this manner, the approach properly accounts for uncertainty about the true number of distinct animals observed in the study. Using a Markov chain Monte Carlo sampling procedure, we conduct a small simulation study to show that this approach has reasonable properties and outperforms other methods. We further illustrate our approach by estimating population size from bobcat photo-identification records. Although motivated by bilateral photo-identification records, we note that the proposed methodology can be used to combine and jointly analyze other types of mark-recapture data (e.g., photo and DNA records).