In order to identify the dynamics regulating growth and viability in a harbour seal population, a combination of analytical and simulation methods is applied.Initially, an equilibrium population with stable age distribution was constructed by means of the age-specific parameters natural mortalities (m), sexual maturities (f) and fertility (b). To prevent indefinite population growth, density dependence was integrated by specifying a carrying capacity (K) of the habitat and alternative parameter values in accordance to changing population densities. A sensitivity analysis was then carried out; measuring which of the demographic parameters had the most profound effects on population growth. To assess this further, we evaluated whether the age structured model may be approximated by the simpler logistic model relying on a single parameter: the intrinsic growth rate. Finally, a more realistic harbour seal population was modelled, in which colonies of various sizes were given different demographic parameter values to mimic the typical source-sink dynamics often found in metapopulations. The effect of environmental stochasticity was explored by running a selection of computer simulations, incorporating random mortality- and emigration rates and catastrophic events.The simulations indicated that population growth is most sensitive to changes in age at sexual maturity along with natural mortality of pups and in seals from two years of ageand older. When comparing the growth models, it became clear that the simple logistic model may be used to describe a population subject to density dependent growth, such asa harbour seal population. However, both models had a severe constraint; the convergence to equilibrium (i.e. carrying capacity) was unrealistically slow. To avoidthis, subsequent simulations assessing metapopulation dynamics were given a constant growth rate. The dynamics of the largest main colony (i.e. source) were independent ofthat of the remaining smaller satellite colonies, acting more or less as sinks. For these colonies, however, the opposite relationship could be observed. Given that the satellitecolonies differed in size and inner recruitment, their dependence of dispersers from the main colony varied correspondingly. Random mortality had the greatest effect on the colonies with the fewest animals, displayed through erratic fluctuations and turnover events (i.e. instability). The largest satellites maintained a relatively stable colony structure provided that the random mortality rates did not exceed the immigration recruitment from the source colony. During the rebuilding process following a catastrophic event, stochastic mortality effectively impeded population growth in all thesatellite colonies. Overall, the simulations demonstrate how different aspects of population dynamics may control population growth and why small or fragmented populations are especially vulnerable to environmental stochasticity.