A general optimal adaptive framework for managing a threatened species

Abstract

Abstract Managers must determine which interventions best protect threatened species when the outcomes of interventions are uncertain. Adaptive management is a dynamic optimization approach that generates optimal management actions based on current knowledge while learning to improve future management outcomes. Although adaptive management theory is well‐developed, uptake has been impeded by its complexity and a tendency to develop bespoke solutions with high implementation costs for problem‐specific returns. To increase uptake of adaptive management and improve threat management for species recovery, we developed a general adaptive management decision model, framed as a Mixed Observability Markov Decision process. We embraced principles of generality, simplicity and interpretability to overcome previous implementation challenges. We created a general model structure that is applicable to any species–threat combination, thus avoiding the need to develop customized models for every species. Simplicity was achieved by minimizing states to reduce the information requirements for parameterization. To improve interpretability, we implemented our method as a Shiny application and employed a recent artificial intelligence approach to simplify the optimal strategy. We applied our approach to a case study of fox impacts on a threatened marsupial. Our case study shows that when one management action is robust to uncertainty, the value of information of optimal adaptive management may be low. Cases like these highlight species–threat combinations where investment in adaptive management is not required. Our tool provides a rapid prototype adaptive management approach with minimal cost to management agencies. Our simple yet general model structure improves efficiency for implementing adaptive management for large numbers of threatened species, improving the effectiveness of conservation investments.

Keywords

• adaptive management
• artificial intelligence
• conservation
• decision theory
• Markov decision process
• MOMDP