By studying animal movements, researchers can gain insight into many of the ecological characteristics and processes important for understanding population-level dynamics. We developed a Brownian bridge movement model (BBMM) for estimating the expected movement path of an animal using discrete location data obtained at relatively short time intervals. The BBMM is based on the properties of a conditional random walk between successive pairs of locations, dependent on the time between locations, the distance between locations, and the Brownian motion variance that is related to the animal's mobility. We describe 2 critical developments that enable widespread use of the BBMM including a derivation of the model when location data are measured with error and a maximum likelihood approach for estimating the Brownian motion variance. After fitting the BBMM to location data, an estimate of the animal's probability-of-occurrence can be generated for an area during the time of observation. To illustrate potential applications, we provide 3 examples including: (1) estimating animal home ranges, (2) estimating animal migration routes, and (3) evaluating the influence of fine-scale resource selection on animal movement patterns.