Understanding the demographic processes driving populations is critical in formulating effective management plans. State-space models offer an appealing framework in which to construct models to examine temporal and spatial variation in population dynamics, as they make efficient use of the varied sources of data available. We develop a Bayesian state-space model of the population dynamics of European blackbird Turdus merula using data collected as part of the British Trust for Ornithology’s Integrated Population Monitoring programme. Previous models of similar data have concentrated on simultaneous estimation of abundance and survival parameters and we extend this approach by incorporating the direct estimation of brood size and nesting success. We show that annual population change is most correlated with adult survival, but that population processes appear to differ in eastern and western regions of Britain which themselves differ markedly in their landscape composition.