Estimating population size with repeated observations of non-invasive sampling in presence of misidentification

Abstract

AbstractAlthough 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 can be collected per individual at one occasion. This is not true for several experiments based on DNA, for example for those that extract the DNA from faecal samples. The 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 account for simultaneous multiple sampling 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 properly the population size in presence of misidentification and repeated observations.