Increasing pressures on the environment are generating an ever-increasing need to manage animal and plant populations sustainably, and to protect and rebuild endangered populations. Effective management requires reliable mathematical models, so that the consequences of management action can be predicted, and the uncertainty in these predictions quantified. These models must be able to predict the response of populations to anthropogenic change, while handling the major sources of uncertainty. We describe a simple building block approach to formulating discrete-time models. These models may include demographic stochasticity, environmental variability through covariates or random effects, multi-species dynamics such as in predator-prey and competition models, movement such as in metapopulation models, non-linear effects such as density dependence, and mating models. We discuss methods for fitting such models to time series of data, and quantifying uncertainty in parameter estimates and population states, including model uncertainty, using computer-intensive Bayesian methods.