Most capture-recapture models assume that individuals either do not emigrate or emigrate permanently from the sampling area during the sampling period. This assumption is violated when individuals temporarily leave the sampling area and return during later capture occasions, which can result in biased or less precise inferences under normal capture-recapture models. Existing temporary emigration models require that individuals are uniquely and correctly identified. To our knowledge, no studies to date have addressed temporary emigration in the presence of latent individual identification, which can arise in many scenarios such as misidentification, data integration, and batch marking. In this paper, we propose a new latent multinomial temporary emigration modelling framework for analysing capture-recapture data with latent identification. The framework is applicable to both closed- and open-population problems, accommodates data with or without individual identification, and flexibly incorporates different emigration processes, including the completely random and Markovian emigration. Through simulations, we demonstrate that model parameters can be reliably estimated in various emigration scenarios. We apply the proposed framework to a real dataset on golden mantella collected using batch marks under Pollock's robust design. The results show that accounting for temporary emigration provides a better fit to the data compared to the previous model without temporary emigration.

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