  Non-invasive marks, including pigmentation patterns, acquired scars,and
genetic mark- ers, are often used to identify individuals in mark-recapture
experiments. If animals in a population can be identified from multiple,
non-invasive marks then some individuals may be counted twice in the observed
data. Analyzing the observed histories without accounting for these errors will
provide incorrect inference about the population dynamics. Previous approaches
to this problem include modeling data from only one mark and combining
estimators obtained from each mark separately assuming that they are
independent. Motivated by the analysis of data from the ECOCEAN online whale
shark (Rhincodon typus) catalog, we describe a Bayesian method to analyze data
from multiple, non-invasive marks that is based on the latent-multinomial model
of Link et al. (2010). Further to this, we describe a simplification of the
Markov chain Monte Carlo algorithm of Link et al. (2010) that leads to more
efficient computation. We present results from the analysis of the ECOCEAN
whale shark data and from simulation studies comparing our method with the
previous approaches.
