CoralBlox: A computationally efficient coral model for decision support

Abstract Coral reef management under climate change is challenging due to data sparsity and high uncertainty, yet it is essential for informing conservation strategies. We present CoralBlox, a mechanistic discrete time coral ecology model with the explicit aim of supporting rapid scenario exploration and decision making. The model represents discretized distributions of five coral functional groups across configurable spatial scales while incorporating key ecological processes, including coral growth, reproduction, thermal adaptation, and responses to disturbances. Validation against observed data demonstrates that CoralBlox effectively captures major trends in coral cover dynamics across the Great Barrier Reef, particularly for bleaching-driven mortality and recovery patterns. While simplifying ecological complexities, the model maintains sufficient ecological realism to evaluate and compare the result of distinct management strategies. CoralBlox enables comprehensive assessment of potential management interventions with high computational efficiency and interoperability. The model’s flexible architecture makes it extensible to coral ecosystems worldwide, providing valuable exploratory capability for reef management. Teaser CoralBlox is an efficient coral reef ecology model supporting rapid scenario testing and management decision making under climate change. Highlights Marine ecosystems are characterized by high uncertainty and data sparsity. Management decisions still need to be made under these uncertain contexts. CoralBlox offers a conceptually simple yet credible representation of ecological processes. Comparatively fast runtimes across different spatial scales enable rapid exploration of plausible future states. Competing Interest Statement The authors have declared no competing interest.