Simulating the impacts of ocean deoxygenation on coral reef resilience using adaptive ecosystem modeling

Adaptive ecosystem modeling provides a proactive approach to mitigating ocean deoxygenation and enhancing coral reef resilience. This study proposes an integrative framework that combines machine-learned dissolved oxygen (DO) reconstructions with a multispecies reef ecosystem model, augmented by Bayesian uncertainty quantification to identify intervention strategies that maximize ecological recovery. By explicitly linking oxygen dynamics with biological feedbacks and management actions, the framework addresses critical gaps in current hypoxia mitigation research. The approach is applied to the central Great Barrier Reef using long-term in situ measurements and satellite observations for model calibration. Simulation results indicate that targeted aeration, watershed nutrient reduction, and microbiome manipulation can reduce cumulative hypoxic stress by up to 43% over two decades, while increasing live coral cover by 28% relative to baseline projections. Sensitivity analyses reveal that model outcomes are particularly influenced by microbial acclimation rates and seasonal DO minima, highlighting the importance of fine-scale biogeochemical monitoring. Overall, the findings demonstrate the value of an adaptive, data-driven decision-support framework that integrates ecological processes, high-resolution environmental data, and management interventions, offering scalable guidance for hypoxia resilience planning in threatened coral reef systems.