Influence of Movement Dynamics on Walleye Harvest Management in Intermixed Fisheries in a Chain of Lakes

Abstract

AbstractFish populations that exhibit movement patterns present challenges to fishery management. In the Inland Waterway in Michigan's northern Lower Peninsula, monitoring Walleye Sander vitreus population dynamics and harvest management is difficult because of seasonal intermixing among interconnected lakes. In addition, the presence of tribal subsistence fishing and recreational angling fisheries that occur during different discrete time periods adds complexity to understanding harvest management performance. We used stochastic simulation to determine the influence of movement and harvest dynamics on the performance of harvest management targets for Walleye in our study system. After accounting for postspawn movement and harvest dynamics, our results indicated that population‐specific exploitation rates on average did not exceed the target rates (u = 0.35) that are mandated in the waterway. We did, however, determine that some areas are at risk because they experienced population‐specific exploitation rates that surpassed the target. We also determined that the interplay between movement and uncertain population and harvest dynamics will likely determine the ability of management to meet currently accepted harvest targets on average over time, as well as the risk of exceeding harvest targets each year. Our findings are broadly applicable for mobile species inhabiting lake chains and highlight that it is critical for managers to gain an understanding of movement as well as harvest dynamics, because both are imperative for understanding how these dynamics influence harvest management performance. As such, we recommend that managers of Walleye populations in other waterways implement tagging studies and harvest monitoring programs to gain an understanding of movement rates and harvest dynamics. An understanding of movement and harvest dynamics along with the stochastic simulation framework we used provides a better understanding of complex system dynamics and leads to informed harvest management decisions.Received July 30, 2016; accepted January 6, 2017 Published online March 31, 2017