State-space models (SSMs) are a class of models which are frequently used to model dynamic systems which involve hidden or unobservable states. SSMs have been increasingly favored in studying animal movements and population dynamics in ecology since they can account for both process variation and observational error. Our study is based on the fish position dataset consisting of detection records from tagged fish (walleye - Sander vitreus) which were collected using a grid of acoustic receivers laid in the bottom of Lake Winnipeg under the “Lake Winnipeg Basin Fish Movement Project” which is being conducted by Fisheries and Oceans Canada. We will assess walleye movement patterns employing broad summaries and individual movement path reconstruction. In this study, the true fish positions are unobserved, we only have the positions of the acoustic receivers detecting them. Hence, we will use the Bayesian State-space modeling approach to reconstruct the true fish movement model by combining it with the observation model describing fish detections. Furthermore, we will study fish behavior by incorporating spatial factors, seasonal effects and their biological information.