Population size estimation when multiple samples carrying the risk of misidentification are taken simultaneously from the same individual

Abstract Although non-invasive sampling is increasingly used in capture-recapture (CR) experiments, it carries a risk of misidentification that, if ignored, causes an overestimation of population size. Models that deal with misidentification have been proposed. However, these models assume that only one sample per individual can be collected at the same occasion. This is not true for several experiments based on DNA, for example for those that extract the DNA from faecal samples. These models do not take repeated observations into account, leading to biased estimates. In this paper, we develop an approach that extends the latent multinomial model (LMM) of Link et al. (2010) using a Poisson distribution to model the number of simultaneous multiple samplings of the same individual. We then conduct simulations to test how our new model performs. As an illustration, we applied the new Poisson model to a collection of Eurasian otter faeces (Lampa et al., 2015). Our findings indicate that repeated observations can be modelled without bias. The application on otters shows that our model is necessary to estimate accurately the population size in presence of misidentification and repeated observations.