A decision framework for prioritizing multiple management actions for threatened marine megafauna.

Resources for conserving biodiversity are invariably insufficient. This situation creates the need for transparent, systematic frameworks to help stakeholders prioritize the allocation of resources across multiple management actions. We developed a novel framework that explicitly prioritizes actions to minimize the impacts of several threats across a species' range. The framework uses a budget constraint and maximizes conservation outcomes from a set of management actions, accounting for the likelihood of the action being successfully applied and accepted by local and Indigenous communities. This approach is novel in that it integrates local knowledge and expert opinion with optimization software, thereby minimizing assumptions about likelihood of success of actions and their effectiveness. To test the framework, we used the eastern Gulf of Carpentaria and Torres Strait population of the flatback turtle, Natator depressus, as a case study. This approach allowed the framework to be applied in a data-poor context, a situation common in conservation planning. The framework identified the best set of actions to maximize the conservation of flatback eggs for scenarios with different budgets and management parameters and allowed comparisons between optimized and preselected scenarios. Optimized scenarios considered all implementable actions to explore how to best allocate resources with a specified budget and focus. Preselected scenarios were used to evaluate current allocations of funds and/or potential budget allocations suggested by different stakeholders. Scenarios that used a combination of aerial and ground strategies to reduce predation of eggs performed better than scenarios that focused only on reducing harvest of eggs. The performances of optimized and preselected scenarios were generally similar among scenarios that targeted similar threats. However, the cost-effectiveness of optimized scenarios was usually higher than that of preselected scenarios, demonstrating the value of conducting a systematic optimization approach. Our method provides a foundation for more effective conservation investments and guidance to prioritize actions within recovery plans while considering the sociopolitical and cultural context of decisions. The framework can be adapted easily to a wide range of species, geographical scales, and life stages.