Publication: Maldives: Decision Support for Coral Reef and Climate Resilience Using Bayesian Networks
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2025-10-21
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2025-11-24
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The coral reef ecosystems of the Maldives are critical to the nation’s ecological integrity, economic development, and climate resilience. As a small island state, the Maldives is heavily dependent on healthy reefs to support tourism, fishing, and coastal protection. However, these ecosystems are increasingly threatened by a combination of global stressors, such as climate change and ocean warming, and local anthropogenic pressures, including overfishing and tourism-related degradation. In this context, tools are needed to support policy analysis and decision-making, evaluating trade-offs between conservation and economic development objectives. This working paper introduces a novel decision support tool built on a Bayesian network modeling framework to evaluate and compare the potential impacts of different policy interventions on reef health. The model was developed using a comprehensive dataset that integrates field-based ecological surveys with satellite-derived environmental and socioeconomic variables. Hard Coral Cover was used as a key indicator of reef health, and the model evaluates several key drivers, including sea surface temperature, human population density, tourism activity, and conservation status. To manage the complexity of these interrelated variables, the model employs a hierarchical structure that includes both manifest (observed) variables and latent (induced) factors. These latent factors capture broader thematic groupings, enabling a “big-picture” understanding of the dynamics of the reef ecosystem. This structure supports transparency and interpretability for stakeholders and decision makers. The decision support tool uses a Bayesian network to perform causal inference, simulating how changes in one part of the system, such as the designation of marine protected areas, affect outcomes throughout the network. Simulation results suggest, for example, that marine protected areas can have a positive impact on coral cover and should be considered as part of a comprehensive reef management strategy. To ensure usability and accessibility, the model has been integrated into an interactive, web based simulator, which is now live and fully operational. This platform allows policymakers, stakeholders, and non-specialists to explore the consequences of various policy choices under different scenarios. Users can visualize trade-offs between ecological and economic outcomes under different policy regimes, such as expanded protection zones, tourism restrictions, or fishing controls. This promotes a deliberate and explicit decision-making process. Beyond introducing the decision support tool itself, the study outlines a broader vision for a national coral reef data hub that would centralize spatial and temporal information from field surveys, remote sensing, and model output. Such an integrated platform would institutionalize evidence-based decision-making, facilitate cross-sectoral coordination, and ensure the continuity of reef monitoring and policy evaluation over time. By fostering sustained engagement with scientific tools and data, this infrastructure could also strengthen local capacity and embed analytical competencies within Maldivian institutions. The current version of the Bayesian Decision Support Tool represents a proof-of-concept prototype rather than a decision-ready product. Future phases will focus on refining the model through structured stakeholder consultations, formal elicitation of local value judgments, and integration into the Digital Maldives for Adaptation, Decentralization, and Diversification (DMADD) climate-resilience data platform. These steps will ensure that the system reflects national priorities, cultural context, and institutional realities, laying the foundation for long-term, sustainable adoption. Emerging Generative AI capabilities, such as AI-assisted knowledge discovery and model parameterization, could further enhance future iterations of the tool, accelerating the synthesis of scientific knowledge and the continuous improvement of causal structures. In conclusion, this study demonstrates how Bayesian network modeling can transform complex environmental data and expert knowledge into a transparent, interpretable, and practical decision-support framework. Combining scientific rigor with flexibility, the approach provides a systematic means to evaluate policy options, balance ecological sustainability with economic development, and guide the long-term management of coral reefs in the Maldives. Embedded within a broader data and policy ecosystem, the tool represents a significant step toward strengthening climate resilience and adaptive capacity in the coastal and marine systems of the country.
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“World Bank. 2025. Maldives: Decision Support for Coral Reef and Climate Resilience Using Bayesian Networks. © World Bank. http://hdl.handle.net/10986/44012 License: CC BY-NC 3.0 IGO.”
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