Abstract
When translocating individuals to found new populations, managers must allocate limited funds among release and monitoring activities that differ in method, cost, and probable result. In addition, managers are increasingly expected to justify the funding decisions they have made. Within the framework of decision analysis, we used robust optimization to formulate and solve different translocation problems in which both population growth and future funding were uncertain. Performance criteria included maximizing mean population size and minimizing the risk of undesirable population‐size outcomes. Robust optimization provided several insights into the design of translocation strategies: (1) risk reduction is obtained at the expense of mean population size; (2) as survival of released animals becomes more important, funds should be allocated to release methods with lower risks of failure, regardless of costs; (3) the performance gain from monitoring drops as the proportion of a fixed budget required to pay for monitoring increases; and (4) as the likelihood of obtaining future funding increases, more of the existing budget should be spent on building release capacity rather than saved for future operating costs. These relationships highlight the importance of performance criteria and economic costs in determining optimal release and monitoring strategies.