Marine conservation and restoration efforts increasingly depend on diverse environmental and ecological datasets, yet these data are often analysed in isolation and used primarily to describe current conditions rather than anticipate future change. This limits the ability of researchers and practitioners to prioritise monitoring, restoration, and intervention efforts under ongoing environmental stress.
We present PMRF (Probabilistic Multimodal Reef Fusion), a predictive modelling framework designed to integrate heterogeneous marine datasets and generate forward-looking insights for coral reef ecosystems. The framework combines bathymetry, underwater imagery and photogrammetry, oceanographic variables, and emerging biological indicators within a probabilistic, uncertainty-aware pipeline. By explicitly modelling uncertainty and accommodating incomplete or uneven data coverage, PMRF is designed to operate under the practical constraints typical of real-world marine datasets.
The development of PMRF builds on multidisciplinary datasets and concepts emerging from ocean exploration initiatives, including work supported through the OceanX Science Impact Challenge. Model outputs include spatial predictions of reef habitat suitability alongside uncertainty estimates, enabling identification of both high-suitability areas and regions where additional data collection would most effectively reduce uncertainty. This dual focus supports strategic planning and adaptive monitoring in data-limited contexts.
While demonstrated using coral reef systems, the approach is designed to be transferable across marine habitats and monitoring contexts. PMRF contributes a methodological framework for integrating predictive analytics into marine biodiversity research, moving beyond static mapping toward anticipatory decision support. Ongoing work focuses on validation with additional datasets and exploring applications for restoration prioritisation and long-term monitoring planning.
This work highlights the potential for multimodal, probabilistic approaches, leveraging insights from large-scale ocean exploration efforts to strengthen data-informed decision-making for marine conservation, restoration, and ecosystem management across Australian and Indo-Pacific reef systems.