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Medusahead is an invasive annual grass that is having persistent and large-scale impacts on western rangelands.� Continued invasion by this and other exotic annuals represents a complex resource management problem that sagebrush rangeland managers must be able to address in order to maintain ecosystem productivity and function. However, medusahead management plays out in a highly variable environment and tactics that are successful in a particular location or year may not yield similar results in a different time or place. �Consequently, traditional shorter term approaches for addressing rangeland management problems do not commonly lend themselves well to solving persistent, complex threats such as medusahead invasion. Rather, addressing the medusahead problem requires longer-term commitment to an adaptive management process.� The science on medusahead control and associated restoration has advanced markedly during recent decades; however, much of this research has been conducted under a limited range of temporal and spatial variability relative to that which rangeland managers encounter in areas being impacted by or at risk of medusahead invasion.� Therefore, the key role of science is to help managers identify a logical starting point for an adaptive management process focused on limiting the risk and associated spread of medusahead and, typically, iterative attempts at restoring areas already invaded. Limited resources generally demand that planned actions be prioritized based on their projected impact, likelihood for success, and cost efficiency. �Restoration of medusahead-invaded rangeland is resource intensive and carries a high risk of failure; therefore, long-term strategies that reduce the spread of medusahead and increase invasion resistance of intact plant communities should receive priority.� Restoration of medusahead-invaded rangeland should be prioritized based on probability of success and value.� Passive and opportunistic restoration strategies should be planned in less valuable areas with a lower probability of success.