  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 effects 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. We show how to estimate the
parameters of such models from time series of data, and how to quantify
uncertainty in those estimates and in numbers of individuals of different types
in populations, using computer-intensive Bayesian methods. We also discuss
advantages and pitfalls of the approach, and give an example using the British
grey seal population.
