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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.

Conservation of marine ecosystems has been highlighted as a priority to ensure a sustainable future. Effective management requires data collection over large spatio-temporal scales, readily accessible and integrated information from monitoring, and tools to support decision-making. However, there are many roadblocks to achieving adequate and timely information on both the effectiveness, and long-term success of conservation efforts, including limited funding, inadequate sampling, and data processing bottlenecks. These factors can result in ineffective, or even detrimental, management decisions in already impacted ecosystems. An automated approach facilitated by artificial intelligence (AI) provides conservation managers with a toolkit that can help alleviate a number of these issues by reducing the monitoring bottlenecks and long-term costs of monitoring. Automating the collection, transfer, and processing of data provides managers access to greater information, thereby facilitating timely and effective management. Incorporating automation and big data availability into a decision support system with a user-friendly interface also enables effective adaptive management. We summarise the current state of artificial intelligence and automation techniques used in marine science and use examples in other disciplines to identify existing and potentially transferable methods that can enable automated monitoring and improve predictive modelling capabilities to support decision making. We also discuss emerging technologies that are likely to be useful as research in computer science and associated technologies continues to develop and become more accessible. Our perspective highlights the potential of AI and big data analytics for supporting decision-making, but also points to important knowledge gaps in multiple areas of the automation processes. These current challenges should be prioritised in conservation research to move toward implementing AI and automation in conservation management for a more informed understanding of impacted ecosystems to result in successful outcomes for conservation managers. We conclude that the current research and emphasis on automated and AI assisted tools in several scientific disciplines may mean the future of monitoring and management in marine science is facilitated and improved by the implementation of automation.

Ocean warming is increasing the incidence, scale, and severity of global-scale coral bleaching and mortality, culminating in the third global coral bleaching event that occurred during record marine heatwaves of 2014-2017. While local effects of these events have been widely reported, the global implications remain unknown. Analysis of 15,066 reef surveys during 2014-2017 revealed that 80% of surveyed reefs experienced significant coral bleaching and 35% experienced significant coral mortality. The global extent of significant coral bleaching and mortality was assessed by extrapolating results from reef surveys using comprehensive remote-sensing data of regional heat stress. This model predicted that 51% of the world’s coral reefs suffered significant bleaching and 15% significant mortality, surpassing damage from any prior global bleaching event. These observations demonstrate that global warming’s widespread damage to coral reefs is accelerating and underscores the threat anthropogenic climate change poses for the irreversible transformation of these essential ecosystems.

Coral reef ecosystems are under increasing pressure from local and regional stressors and a changing climate. Current management focuses on reducing stressors to allow for natural recovery, but in many areas where coral reefs are damaged, natural recovery can be restricted, delayed or interrupted because of unstable, unconsolidated coral fragments, or rubble. Rubble fields are a natural component of coral reefs, but repeated or high-magnitude disturbances can prevent natural cementation and consolidation processes, so that coral recruits fail to survive. A suite of interventions have been used to target this issue globally, such as using mesh to stabilise rubble, removing the rubble to reveal hard substrate and deploying rocks or other hard substrates over the rubble to facilitate recruit survival. Small, modular structures can be used at multiple scales, with or without attached coral fragments, to create structural complexity and settlement surfaces. However, these can introduce foreign materials to the reef, and a limited understanding of natural recovery processes exists for the potential of this type of active intervention to successfully restore local coral reef structure. This review synthesises available knowledge about the ecological role of coral rubble, natural coral recolonisation and recovery rates and the potential benefits and risks associated with active interventions in this rapidly evolving field. Fundamental knowledge gaps include baseline levels of rubble, the structural complexity of reef habitats in space and time, natural rubble consolidation processes and the risks associated with each intervention method. Any restoration intervention needs to be underpinned by risk assessment, and the decision to repair rubble fields must arise from an understanding of when and where unconsolidated substrate and lack of structure impair natural reef recovery and ecological function. Monitoring is necessary to ascertain the success or failure of the intervention and impacts of potential risks, but there is a strong need to specify desired outcomes, the spatial and temporal context and indicators to be measured. With a focus on the Great Barrier Reef, we synthesise the techniques, successes and failures associated with rubble stabilisation and the use of small structures, review monitoring methods and indicators, and provide recommendations to ensure that we learn from past projects.