and ecological impacts. Therefore, monitoring data is not optimized to inform the outcomes of invasive species control or other management actions, thereby limiting the decision relevance of monitoring efforts and precluding the development of formalizing learning. Learning can be formalized using analytical approaches, such as Bayesian belief networks, to employ iterative updating (i.e., Bayesian learning) and monitor data to inform the iterative application of invasive species control or management actions, thereby meeting natural resource agency management objectives. Integrating monitoring of invasive species dynamics with monitoring of concurrent economic and ecological impacts is required to maximize the return on monitoring investment and maximize the value of monitoring data for iterative learning, which in turn is needed to maximize the effectiveness of control or management actions.This project will fill a knowledge gap in efficient, effective, and economic monitoring approaches, with an emphasis on informing the effectiveness of management actions intended to control invasive species and minimize economic impacts in the Mississippi River Basin. Specifically, once an invasive species invades an aquatic system, existing monitoring continues to follow established protocols that are suboptimal in informing the effectiveness of management actions. Developing frameworks that iteratively update from accumulated monitoring efforts can adapt to what is learned, maximizing the return on investment in evaluating the effectiveness of management actions intended to control invasive aquatic species. Frameworks like this are necessary for Mississippi River basin fishery and aquatic resource managers to efficiently, effectively, and economically monitor, with an emphasis on informing the effectiveness of management actions intended to control invasive species and minimize economic impacts.