The timing of spawning in fish is tightly linked to environmental  patterns, rhythms and cues.  Biologists believe that spawning is governed by both biological and behavioral factors.  However, these factors are not very well understood and thus little information is available to guide recruitment efforts for endangered species such as the sturgeon.  Therefore, we propose a Bayesian hierarchical model for predicting spawning success of the shovelnose sturgeon which uses both biological (cross-sectional) and behavioral (longitudinal) data.  In particular, we use data produced from a tracking study conducted in the Lower Missouri River.  The data produced from this study consist of biological variables associated with readiness to spawn along with longitudinal behavioral data collected using telemetry and data storage device sensors.  These high frequency data are complex both biologically and in the underlying behavioral process.  To accommodate such complexity the model we develop uses an eigenvalue predictor, derived from the transition probability matrix of a two-state Markov switching model with GARCH dynamics, as a generated regressor in a hierarchical linear regression model.  Finally, in order to minimize the computational burden associated with estimation of this model, a parallel computing approach is proposed.