We investigated the utility of state-space models for determining the demographic causes of population declines, using the Song Thrush as an example. A series of integrated state-space models were fitted to census and ring-recovery data from the United Kingdom for the period 1968-2000. The models were fitted using Bayesian MCMC techniques with uniform priors and were ranked using the Deviance Information Criterion (DIC). Ring-reporting rates were modelled as a declining logit-linear function of year, with separate slopes for first-year birds and adults. The system process involved three demographic parameters, first-year survival, adult survival and productivity. Survival rates were modelled as year-specific, as specific to blocks with uniform population growth rates, or as logit-linear functions of weather or year. Productivity rates were modelled as random annual effects, as block-specific or as log-linear functions of year. We fitted 17 such models chosen on the basis of our prior knowledge of this system, given that it was not practical to fit all potential models. Six models within 10 points of the smallest DIC were selected for inference. The posterior distributions from these preferred models suggest that population growth are bext correlated with first year survival and that there is also a pattern of consistent but weaker correlations between population growth rate and adult survival. Correlations between population growth rates and productivity were more variable, and may have been influenced by errors in other parts of the model, as productivity is essentially measured by difference. This in this analysis the evidence for productivity having a ]substantial influence on population changes is equivocal. The interpretation of these results and the potential value of integrated state-space models for research into the population dynamics of declining populations are discussed.