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While non-invasive sampling is more and more commonly used incapture-recapture (CR) experiments, it carries a higher risk ofmisidentifications than direct observations. As a consequence, one must screenthe data to retain only the reliable data before applying a classical CR model.This procedure is unacceptable when too few data would remain. Models able todeal with misidentifications have been proposed but are barely used. Threeobjectives are pursued in this paper. First, we present the Latent MultinomialModel of Link et al. (2010) where estimates of the model are obtained from aMonte Carlo Markov Chain (MCMC). Second we show the impact of the use of aninformative prior over the estimations when the capture rate is low. Finally weextend the model to the multistate paradigm as an example of its flexibility.  We showed that, without prior information, with capture rate at 0.2 or lower,parameters of the model are difficult to estimate i.e. either the MCMC does notconverge or the estimates are biased. In that case, we show that adding aninformative prior on the identification probability solves the identifiabilityproblem of the model and allow for convergence. It also allows for good qualityestimates of population size, although when the capture rate is 0.1 itunderestimates it of about 10%. A similar approach on the multistate extensionshow good quality estimates of the population size and transition probabilitieswith a capture rate of 0.3 or more.

Population size estimation with capture-recapture in presence of individual misidentification and low recapture