  The analysis of telemetry data is common in animal ecological studies. While
the collection of telemetry data for individual animals has improved
dramatically, the methods to properly account for inherent uncertainties (e.g.,
measurement error, dependence, barriers to movement) have lagged behind. Still,
many new statistical approaches have been developed to infer unknown quantities
affecting animal movement or predict movement based on telemetry data.
Hierarchical statistical models are useful to account for some of the
aforementioned uncertainties, as well as provide population-level inference,
but they often come with an increased computational burden. For certain types
of statistical models, it is straightforward to provide inference if the latent
true animal trajectory is known, but challenging otherwise. In these cases,
approaches related to multiple imputation have been employed to account for the
uncertainty associated with our knowledge of the latent trajectory. Despite the
increasing use of imputation approaches for modeling animal movement, the
general sensitivity and accuracy of these methods have not been explored in
detail. We provide an introduction to animal movement modeling and describe how
imputation approaches may be helpful for certain types of models. We also
assess the performance of imputation approaches in a simulation study. Our
simulation study suggests that inference for model parameters directly related
to the location of an individual may be more accurate than inference for
parameters associated with higher-order processes such as velocity or
acceleration. Finally, we apply these methods to analyze a telemetry data set
involving northern fur seals (Callorhinus ursinus) in the Bering Sea.
