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The collection of capture-recapture data often involves collecting data onnumerous capture occasions over a relatively short period of time. For manystudy species this process is repeated, for example annually, resulting incapture information spanning multiple sampling periods. The robust design classof models provide a convenient framework in which to analyse all of theavailable capture data in a single likelihood expression. However, these modelstypically rely either upon the assumption of closure within a sampling period(the closed robust design) or condition on the number of individuals capturedwithin a sampling period (the open robust design). The models we develop inthis paper require neither assumption by explicitly modelling the movement ofindividuals into the population both within and between the sampling periods,which in turn permits the estimation of abundance. These models are furtherextended to allow parameters to depend not only on capture occasion but alsothe amount of time since joining the population and to the case of multi-statedata where there is individual time-varying discrete covariate information. Wederive an efficient likelihood expression for the new multi-state multi-periodstopover model using the hidden Markov model framework. We demonstrate the newmodel through a simulation study before considering a dataset on great crestednewts, Triturus cristatus.

Estimating abundance from multiple sampling capture-recapture data via a multi-state multi-period stopover model