POPULATION DYNAMICS WITHOUT INDIVIDUAL IDENTIFICATION: HIDDEN MARKOV MODELING OF BATCH-MARKING DATA
Batch marking provides an important and efficient way to estimate the survival probabilities and abundance of wild populations of fishes, amphibians and insects. It occurs when animals are marked in batches, but individuals are not distinguished, therefore providing an alternative to classical capture-recapture methods. An extended batch-marking experiment is one where individuals caught at the same sample time are given the same batch tag (colour for example). In this talk, we develop a modeling framework for extended batch-marking experiments that makes explicit the distinction between the biological state variables (abundance, survival) and the observation process – so-called hidden Markov models. We also include ways of modeling unmarked individuals in the population. We built on recent developments in statistical ecology to show that marked and unmarked individuals can be combined using integrated population modelling. This allows the simultaneous estimation of population size and immigration, in addition to survival, as well as efficient evaluation of uncertainty and model comparison, using standard likelihood techniques. An illustration is provided by an oriental weather loach (Misgurnus anguillicaudatus) data set.