Invasive species management has emerged as a high-priority issue in planning the restoration and conservation of the Greater Everglades. Management agencies are currently involved in a number of management activities such as coordination of operational control efforts and monitoring of many invasive species. Given the limited amount of resources to control invasive species and their potential to impose great damage to ecological systems and ecosystems services, it is important to maximize efficiency of control and monitoring efforts. Decision theory provides a framework for improving the efficiency of the control of invasive species. We focus on the application of decision theory to two case studies in the Greater Everglades: 1) Tegus (Tupinambis spp.) are lizards native to South America that have become established in Florida. The focus is on containing the population to prevent expansion into Everglades National Park. In August 2015 we met with state and federal members of the research and management communities to identify management objectives, potential control actions, and information needed to control the spread of tegus in Florida. We developed a matrix population model for tegus, and a spatial optimization algorithm to help inform control, monitoring, and research efforts. 2) Control of the invasive melaleuca (Melaleuca quinquenervia) in the Everglades has met some success, but managers are interested in more cost-effective control. We considered the application of optimization methods to identify the most cost-effective application of treatments.
Results/Conclusions
Before considering applying sophisticated optimization methods it is often helpful to explore the dynamics of the ecological system of interest and particularly evaluate its response to potential management actions. We used a matrix population modeling approach to examine the population dynamics of tegus and investigated alternative scenarios to assess the cost necessary to stabilize the population of tegus. We then applied a spatial optimization method to determine where, when and at what intensity to trap tegus.
With respect to melaleuca, we focused on the application of stochastic dynamic programming to derive decision rules for treatment. The optimization approach that we consider accounts for important sources of uncertainty, such as environmental uncertainty, and scientific uncertainty. In fact, we show how scientific uncertainty can be reduced with the application of an adaptive management approach. This approach to management is particularly appealing to managers because it can be shown that the purpose of learning about the system in this context is explicitly focused on achieving management objectives.