From business intelligence to conservation intelligence: Operationalizing adaptive pest control of crown‐of‐thorns starfish to protect the resilience of the Great Barrier Reef

Abstract

Abstract Resilience‐based management (RBM)—which focuses explicitly on preserving and actively enhancing an ecosystem's capacity to resist and recover from cumulative disturbances under climate change—is crucial for enhancing outcomes in conservation interventions as the climate changes. To be effective it requires continuous modeling, assessment, evaluation and adjustment. Here, we deploy enterprise Business Intelligence software for the purpose of conservation (i.e., Conservation Intelligence) to provide the near real‐time analytics and decision support system necessary for effective RBM. This approach is demonstrated using the Crown‐of‐Thorns Starfish Control Program on Australia's Great Barrier Reef where integrated visual dashboards were developed to assess outbreak severity, prioritize control actions, evaluate effectiveness and incorporate emerging research to close the research‐implementation gap. The flexibility of Business Intelligence software allows these Conservation Intelligence tools to be built and maintained “in‐house,” meeting the Great Barrier Reef Marine Park Authority's (GBRMPA) explicit needs and reducing dependency on external developers or researchers. Conservation Intelligence tools can synthesize complex spatial–temporal data into flexible, user‐friendly platforms specifically targeting stages of the RBM cycle that enable rapid iterations as programs and ecosystems adapt to climate change. Importantly, this approach is platform‐agnostic and a methodological approach that leverages existing enterprise infrastructure to establish rapid, in‐house solutions. This approach is readily transferable to other conservation challenges, particularly in government‐led programs where enterprise software licenses may already exist and is particularly useful in ecologically complex but data rich environments.

The authors' abstract, as published at the source. Conservation Science and Practice, 2026 · DOI ↗