Predicting Coral Cover Trends From Local to Broad Spatial Scales

Modern biodiversity monitoring programs are designed to provide rapid assessments of trends in the abundance and distribution of keystone taxa and timely scientific insights for decision-making. An important consideration when delivering this information to stakeholders is the quantification of uncertainty, which determines the robustness of detecting changes across habitats and regions. In coral reef science, sparse and fragmented monitoring datasets hinder assessments of reef habitat changes. We introduce a comprehensive prediction framework for estimating trends in hard coral cover and associated uncertainty at a local scale (5 km² predictive cells). The model accounts for spatial and temporal dependencies in coral cover through a latent process formulation, where correlation is structured explicitly in space and time, and includes environmental variables describing exposure to heat stress and tropical cyclones. We use a weighted spatial aggregation approach to predict trends at subregional and regional scales, with subregional and regional extents varying according to geographic context. The same approach is applied to estimate trends at broader spatial scales by combining outputs from multiple models through the ReefCloud platform, ensuring that predictions reflect the spatial distribution of reef-building corals and that uncertainty is appropriately propagated across spatial scales. The model also quantifies effects of heat stress and tropical cyclones and characterizes their associations with coral cover change across gradients of disturbance intensity. We demonstrate the value of the framework using two use cases: the central Great Barrier Reef in Australia and American Samoa. Together, these applications highlight the potential of our integrated approach as a widely applicable method for predicting coral cover trends at various spatial scales. We also discuss the substantial uncertainty associated with the framework due to the limitations of available datasets and suggest approaches to improve the robustness of trend detection for coral reefs and their attribution to environmental disturbances.