Operationalising resilience-based management at scale: A meta-adaptive blueprint from the Great Barrier Reef crown-of-thorns starfish control program
Abstract
Resilience-based management (RBM) has been widely adopted as a future focused extension of adaptive management to address mounting climate change impacts on coral reef ecosystems, yet there are few demonstrated examples of RBM operating effectively at large spatial and institutional scales. The Crown-of-Thorns Starfish (COTS) Control Program on the Great Barrier Reef illustrates how RBM can be operationalised by incrementally building new dimensions of the Program onto a simple foundation of direct management action. We term this approach meta-adaptive management: a deliberate process in which an intervention program incrementally expands its scope, sophistication and its capacity to adapt over time through stakeholder engagement, technical refinement, and effective governance. Rather than assuming a fully mature adaptive framework is in place from the outset, meta-adaptive programs build the institutional, social, and technical capacity required for RBM to function at scale while continuing to deliver operational outcomes. We describe how this approach has been applied in the COTS Control Program, with a specific focus on recent advances in reef prioritisation. We also distil eight transferable enabling components that are built over time—foundational research, systematic monitoring, technical efficacy, stakeholder and political support, governance and strategy, secure funding, decision-support systems, and robust prioritisation—and show how recurring decision points (e.g., annual prioritisation) create incentives for applied research and stakeholder alignment. This perspective offers a practical blueprint for conservation programs facing dynamic threats and uncertain futures.
Fields of Study & Subjects
Coral and Marine Ecosystems Studies
Coastal and Marine Management
Marine and fisheries research
Publication details
- Journal / source
- Biological Conservation
- Publisher
- Elsevier BV
- Volume
- 321
- Pages
- 111910–111910
- Publication date
- 10 June 2026
- Language
- EN