Assessing movement patterns using Bayesian state space models on Lake Winnipeg walleye

Abstract

Acoustic telemetry systems technology is useful for studying fish movement patterns and habitat use; however, the data generated from omnidirectional acoustic receivers are prone to large observation errors because the tagged animal can be anywhere in the detection range of the receiver. In this study, we used the Bayesian state space modeling (SSM) approach and different smoothing methods including kernel smoothing and cross-validated local polynomial regression to reconstruct fish movement paths of walleye (Sander vitreus) using data obtained from a telemetry receiver grid in Lake Winnipeg, Manitoba, Canada. Using the SSM approach, we obtained more realistic movement paths, compared with the smoothing methods. In addition, we have highlighted the advantages of the SSM approach to estimate undetected movement paths, over simple smoothing techniques, by comparing ecological metrics such as path length and tortuosity between different reconstruction approaches. Reconstructed paths could be useful in making effective fishery management decisions on Lake Winnipeg in the future by providing information on how walleye move and distribute in Lake Winnipeg over space and time.