  New methods for modeling animal movement based on telemetry data are
developed regularly. With advances in telemetry capabilities, animal movement
models are becoming increasingly sophisticated. Despite a need for
population-level inference, animal movement models are still predominantly
developed for individual-level inference. Most efforts to upscale the inference
to the population-level are either post hoc or complicated enough that only the
developer can implement the model. Hierarchical Bayesian models provide an
ideal platform for the development of population-level animal movement models
but can be challenging to fit due to computational limitations or extensive
tuning required. We propose a two-stage procedure for fitting hierarchical
animal movement models to telemetry data. The two-stage approach is
statistically rigorous and allows one to fit individual-level movement models
separately, then resample them using a secondary MCMC algorithm. The primary
advantages of the two-stage approach are that the first stage is easily
parallelizable and the second stage is completely unsupervised, allowing for a
completely automated fitting procedure in many cases. We demonstrate the
two-stage procedure with two applications of animal movement models. The first
application involves a spatial point process approach to modeling telemetry
data and the second involves a more complicated continuous-time discrete-space
animal movement model. We fit these models to simulated data and real telemetry
data arising from a population of monitored Canada lynx in Colorado, USA.
