Reframing wildlife disease management problems with decision analysis.

Area of Science:

• Wildlife disease ecology
• Conservation science
• Decision analysis

Background:

• Wildlife disease management is complex, involving diverse stakeholders, uncertainties, and trade-offs.
• Current approaches often oversimplify decision-making by focusing solely on scientific knowledge, neglecting broader contexts.
• This can lead to management strategies that are detached from practical decision-making needs.

Purpose of the Study:

• To propose reframing wildlife disease management as decision problems.
• To advocate for the use of decision analytical tools to address these complexities.
• To enhance the quality, rigor, and transparency of wildlife disease management decisions.

Main Methods:

• Conceptual framework development for decision-based wildlife disease management.
• Application of structured decision-making (SDM) principles.
• Illustrative case studies including severe acute respiratory syndrome coronavirus 2, white-nose syndrome, avian influenza, and chytridiomycosis.

Main Results:

• Identified common impediments to effective decision-making: competing objectives, risks, prediction uncertainty, and resource limitations.
• Demonstrated how SDM can break down complex problems into manageable elements.
• Highlighted the potential for SDM to improve relevance and applicability of scientific information.

Conclusions:

• Wildlife disease management requires a shift from a purely scientific problem framing to a decision-problem approach.
• Structured decision-making offers a robust framework for navigating complex wildlife disease challenges.
• Adopting decision analytical tools can lead to more effective, transparent, and contextually relevant management outcomes.