El Niño, Sea Surface Temperature Anomaly and Coral Bleaching in the South Atlantic: A Chain of Events Modeled With a Bayesian Approach

• Published in: Journal of geophysical research. Oceans 2018-04, Vol.123 (4), p.2554-2569
• Main Authors: Lisboa, D. S., Kikuchi, R. K. P., Leão, Zelinda M. A. N.
• Format: Article
• Language: English
• Subjects: Abrolhos reefs; Anomalies; Bayesian analysis; Bayesian network; Climate change; Coral bleaching; coral bleaching forecast; Corals; Ecosystems; El Nino; El Nino phenomena; ENSO; Environmental indicators; Geophysics; Heating; Indicators; Mathematical models; Modelling; Prediction models; Probability theory; Reefs; Remote sensing; Sea surface; Sea surface temperature; Sea surface temperature anomalies; Summer; Surface temperature; Temperature anomalies; thermal index; Thermal stress; Tracking; Turbidity
• ISSN: 2169-9275; 2169-9291
• DOI: 10.1002/2017JC012824

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. Key Points The cause and effect relationships between El Niño, thermal indices and field data were used to construct a bleaching forecast network The Bayesian network uses information from six environmental indicators to generate seasonal forecasts of bleaching events in a study site The model was used to investigate bleaching in relation to turbidity, to indicate areas less prone to suffering bleaching and to predict a 2016 bleaching event