Use of Bayesian models to develop coral bleaching indices and forecasts from in-situ observations for the 2015–16 bleaching event

Abstract

Abstract Bayesian models provide simple conceptual probabilistic models of systems and system processes. Using real time data from IMOS Wireless Sensor Networks at four sites along the Great Barrier Reef, two Bayesian models were built to provide daily indices of current and future bleaching risk for the 2015–16 coral bleaching event. The first model used real time measures of water temperature and light, calculated degree-heat-week values and empirical bleaching thresholds to model the heat and light stress to produce a current bleaching risk index. The second model incorporated factors known to change bleaching risk, such as winds, tides and rainfall, to model the likely change in bleaching risk for the near future. The models used daily values from four IMOS Wireless Sensor Network sites, spread from north to south, for the 2015–16 bleaching event with the outputs delivered to the coral reef community via a dedicated web site. The model outputs were subsequently compared to aerial bleaching surveys with the models providing a good measure of the bleaching risk and, importantly, giving 2–3 days warning of extreme events such as the bleaching of northern reefs. These warnings were used to drive observational work to capture data from the bleaching event, an event that impacted a third of the Great Barrier Reef. Bayesian models provide easy to understand process or system models that can be quickly developed using a range of available inputs, including anecdotal and low-quality sources, to produce management relevant indices.