The construction of causal networks to estimate coral bleaching intensity

  • Authors:

  • Lilian Anne Krug;Douglas Francisco Marcolino Gherardi;José LuíS Stech;Zelinda Margarida Andrade Nery LeãO;Ruy Kenji Papa Kikuchi;Estevam Rafael Hruschka, Junior;David John Suggett

  • Affiliations:

  • National Institute for Space Research, Remote Sensing Division, Avenida dos Astronautas, 1758, Zip Code: 12227-010 São José dos Campos, SP, Brazil and University of the Algarve, Centre f ...;National Institute for Space Research, Remote Sensing Division, Avenida dos Astronautas, 1758, Zip Code: 12227-010 São José dos Campos, SP, Brazil;National Institute for Space Research, Remote Sensing Division, Avenida dos Astronautas, 1758, Zip Code: 12227-010 São José dos Campos, SP, Brazil;Federal University of Bahia, Institute of Geosciences, R. Barão de Jeremoabo s/n, Zip Code: 40170-115 Salvador, BA, Brazil;Federal University of Bahia, Institute of Geosciences, R. Barão de Jeremoabo s/n, Zip Code: 40170-115 Salvador, BA, Brazil;Federal University of São Carlos, Computer Science Department, Rod. Washington Luis km 235, Zip Code: 13565-905 São Carlos, SP, Brazil;University of Essex, Department of Biological Sciences, Wivenhoe Park, Colchester CO43SQ Essex, United Kingdom

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Current metrics for predicting bleaching episodes, e.g. NOAA's Coral Reef Watch Program, do not seem to apply well to Brazil's marginal reefs located in Bahia state and alternative predictive approaches must be sought for effective long term management. Bleaching occurrences at Abrolhos have been observed since the 1990s but with a much lower frequency/extent than for other reef systems worldwide. We constructed a Bayesian Belief Network (BN) to back-predict the intensity of bleaching events and learn how local and regional scale forcing factors interact to enhance or alleviate coral bleaching specific to Abrolhos. Bleaching intensity data were collected for several reef sites across Bahia state coast (~12^o-20^oS; 37^o-40^oW) during the austral summer 1994-2005 and compared to environmental data: sea surface temperature (SST), diffuse light attenuation coefficient at 490 nm (K"4"9"0), rain precipitation, wind velocities, and El Nino Southern Oscillation (ENSO) proxies. Conditional independence tests were calculated to produce four specialized BNs, each with specific factors that likely regulate bleaching intensity. All specialized BNs identified that a five-day accumulated SST proxy (SSTAc5d) was the exclusive parent node for coral bleaching producing a total predictive rate of 88% based on SSTAc5d state. When SSTAc5d was simulated as unknown, the Thermal-Eolic Resultant BN kept the total predictive rate of 88%. Our approach has produced initial means to predict beaching intensity at Abrolhos. However, the robustness of the model required for management purposes must be further (and regularly) operationally tested with new in situ and remote sensing data.