Population monitoring is a vital component for managing threatened and endangered (TE) species to demonstrate recovery, or alert managers if the status is deteriorating. Common methods for analyzing monitoring data, however, have poor power to detect changes in population status and do not directly address questions about population status as defined for threatened (likely to be endangered) or endangered (in danger of extinction) species. Population viability analysis (PVA) methods are used to estimate the risk of decline for population, and have been recommended to reconcile short-term management actions with the ultimate long-term goal of preserving the species. The research presented herein is concerned with how to use PVA for monitoring population status, with a general focus on TE species and specific application to a bull trout (Salvelinus confluentus) population. The bull trout population in the Flathead Lake and River System of NW Montana, USA provides an example for the motivation of VPM, and will serve as a test bed for developing and applying VPM for population management. Bull trout are listed as threatened under the Endangered Species Act, and the Flathead Lake population is of special concern because of a dramatic decline in the late 1980\'s. Risk estimates are constrained by the ability to estimate model parameters from data. I develop methods to accommodate sampling error in population data and temporal correlations in population growth for count-based PVA models, and evaluate the effects of model extrapolation errors on risk estimates. Further, I present model structural adequacy analyses in which model evaluation criteria are not based on a model\'s fit to data, but on how well the model answers the scientific question of interest concerning the population\'s future status. This study suggests monitoring with a Gompertz density dependent model is likely the best available means for estimating average risk of decline for the bull trout population. Data on juvenile vital rates and abundances incorporated into a relatively simple demographic model could potentially enhance the ability to foresee imminent declines in adult abundances, though risk estimates can be detrimentally affected by uncertainty in sub-adult survival rates.