Coral bleaching represents one of the main climate‐change related threats to reef ecosystems. This research represents a methodological alternative for modeling this phenomenon, focused on assessing uncertainties and complexities with a low number of observations. To develop this model, intermittent reef monitoring data from the largest reef complex in the South Atlantic collected over nine summers between 2000 and 2014 were used with remote sensing data to construct and train a bleaching seasonal prediction model. The Bayesian approach was used to construct the network as it is suitable for hierarchically organizing local thermal variables and combining them with El Niño indicators from the preceding winter to generate accurate bleaching predictions for the coming season. Network count information from six environmental indicators was used to calculate the probability of bleaching, which is mainly influenced by the combined information of two thermal indices; one thermal index is designed to track short period anomalies in the early summer that are capable of triggering bleaching (SST of five consecutive days), and the other index is responsible for tracking the accumulation of thermal stress over time, an index called degree heating trimester (DHT). In addition to developing the network, this study conducted the three tests of applicability proposed for model: 1‐ Perform the forecast of coral bleaching for the summer of 2016; 2‐ Investigate the role of turbidity during the bleaching episodes; and 3‐ Use the model information to identify areas with a lower predisposition to bleaching events.
Coral bleaching in the North Atlantic Ocean was modeled based on the historical relationship between reef observations , El Niño indicators and thermal seawater anomalies. The model components were hierarchically organized into a Bayesian network structure according to their level of influence on coral bleaching to generate seasonal predictions to be confirmed (or not) by the near-real-time forecast at~5 km scale. Validations and score steps, used as a criterion for comparison between competing models, proved the viability of the Bayesian approach to perform seasonal forecasts of the bleaching occurrence, achieving an overall hit rate of 84%. Custom models with databases restricted to specific situations are presented as an alternative to improve the accuracy levels but at the cost of the loss of predictive ability. The models were developed to be conceptually simple and useful tools to assist environmental management through an early warning system for coral bleaching.Plain Language Summary We developed a model capable of carrying out seasonal forecasts based on the assumption that coral bleaching in the North Atlantic Ocean is caused by a series of concatenated phenomena that begin with the El Niño phenomenon and manifest in the study area as temperature anomalies. Thus, we built a Bayesian network from the cause-effect relationships between a set of relevant environmental variables and coral bleaching observations collected over 24 years. The model presented a coherent ordering of variables that allowed over 80% accurate predictions of the coral bleaching occurrence. In addition, models constructed for specific situations were even more accurate, suggesting the applicability of the model as a potentially useful tool for performing reliable short-term coral bleaching predictions.
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