Recent technological advances have permitted the collection of detailed animal location and ancillary biotelemetry data that facilitate inference about animal movement and associated behaviors. However, these rich sources of individual information, location and biotelemetry data, are typically analyzed independently, with population-level inferences remaining largely post hoc. We describe a hierarchical modeling approach which is able to integrate location and ancillary biotelemetry (e.g. physiological or accelerometer) data from many individuals. We can thus obtain robust estimates of: 1) population-level movement parameters; and 2) activity budgets for a set of behaviors among which animals transition as they respond to changes in their internal and external environment. Measurement error and missing data are easily accommodated using a state-space formulation of the proposed hierarchical model. Using Bayesian analysis methods, we demonstrate our modeling approach with location and dive activity data from 17 harbor seals (Phoca vitulina) in the UK. Based jointly on movement and diving activity, we identified three distinct movement behavior states: "resting", "foraging" and "transit", and estimated population-level activity budgets to these three states. Because harbor seals are known to dive for both foraging and transit (but not usually for resting), we compared these results to a similar population-level analysis utilizing only location data. We found a large proportion of time steps were mischaracterized when behavior states were infeered from horizontal trajectory alone, wuth 33% of time steps exhibiting a dive activity assigned to a resting state. Only 1% of these time steps were assigned to resting when inferred from both trajectory and dive activity data using our integrated modeling approach. There is mounting evidence of the potential perils of inferring animal behavior based on trajectory alone, but there fortunately now exist many flexible analytical techniques for extracting more out of the increasing wealth of information afforded by recent advances in biologging technology.