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Two Sustainable Development Goals are focused directly on combating the impacts of climate change on coral reef communities. These are: providing cost-effective and timely information to reef managers (SDG 13 - climate action) and sustaining healthy coral reefs in the future (SDG 14 - life below water). Citizen science (CS) features prominently in a range of programs that have been developed to address these goals. One such program is Virtual Reef Diver, which is designed to help monitor the health of the Great Barrier Reef in Australia. This program engages citizen scientists in two ways. Scuba-divers are asked to take geo-coded underwater images of the reef and upload them to an online virtual reef. Members of the public across the world are then asked to classify these images with respect to key reef indicators such as coral. Through the lens of a Virtual Reef Diver event held as part of 2021 Australia’s National Science Week, we describe important features of this program that positively address common concerns about CS data, including the scientific trustworthiness of the data, the ability to incorporate these data with other more traditional data sources, and the quantifiable improvement in information about reef health using these data for management decisions. This demonstrates the important role that citizen science can play in achieving SDGs.

As coral reefs endure increasing levels of disturbance, understanding patterns of recovery following disturbance(s) is paramount to assessing the sustainability of these ecosystems. Given the slow dynamics of coral reefs and the increasing frequency of environmental pressures, management strategies focus on understanding recovery patterns to drive efforts and actively promote the recovery of key coral populations. However, the fine spatial scale heterogeneity of coral dynamics challenges our capacity to understand recovery patterns at large spatial scales and guide effective management actions. In this study, we developed a spatio-temporal statistical model to estimate the long-term trajectories of branching, plate and massive corals at fine-spatial scales and predict their recovery patterns at unobserved locations within a reef. We parameterized the model using repeated and georeferenced observations from 783 locations during 16 years at Heron Reef (Great Barrier Reef, Australia). We then developed indicators of recovery that capture the interplay between coral growth and relative decline from disturbance(s) across time, space and growth morphology. Our results reveal that successful recoveries, expressed in terms of probability, are associated with minimum growth rate thresholds of 4.3\% and 6.4\% (absolute growth, y-2) for branching and plate corals in reef locations that were impacted by disturbance(s) at medium-high levels and historically abundant. As a product of the data revolution, predictive maps from statistical models support the development of new indicators that can support the identification of areas of concern to prioritise management intervention. They should be used into larger spatially explicit modelling framework for decision-making in reef conservation and restoration.