parameters which results in efficient MCMC sampling. MCMC efficiency is defined as the number of effectively independent posterior samples generated per second of algorithm run- time, which balances improvements in MCMC mixing with computational requirements. This automated blocking procedure is described in detail in Turek et al. (2015). The use of a block sampling strategy can be combined with filtering over latent states. Under this approach we use the filtering algorithms already described to integrate out the latent states, and require MCMC sampling for the model parameters. We use a dynamically determined block sampling strategy for the MCMC sampling of these parameters. We use three capture-recapture examples representing different levels of complexity to asses performance of the various computational approaches to MCMC estimation. The first is the well-studied European Dipper dataset, demonstrating single-state capture-recapture. The second is a multistate capture-recapture dataset of observations of a flowering orchid. This is considered multistate data since the orchids may be observed in multiple distinct states, in addition to the possibility of “not seen”. The third and largest dataset is also a multistate example, representing observations of Canadian Geese at various locations. The European Dipper ( Cinclus cinclus ) dataset has been analyzed extensively in the liter- ature (Marzolin, 1988; Lebreton et al., 1992; Gimenez et al., 2007; Royle, 2008; Amstrup, McDonald, and Manly, 2010, among numerous others), and may be considered a canonical example of capture-recapture. For simplicity, we do not make use of a covariate reflecting gender or the distinction of flood years as in Lebreton et al. (1992). The dataset consists of n = 294 sighting histories collected over k = 7 annual sighting occasions. The set of latent states is X = { “alive” , “dead” } and the set of observable states 11 3.1 Dipper Model 3 Capture-Recapture Example Models