Coral Reefs

, Volume 23, Issue 1, pp 96–108

Learning to predict large-scale coral bleaching from past events: A Bayesian approach using remotely sensed data, in-situ data, and environmental proxies

Report

DOI: 10.1007/s00338-003-0361-y

Cite this article as:
Wooldridge, S. & Done, T. Coral Reefs (2004) 23: 96. doi:10.1007/s00338-003-0361-y

Abstract

Ocean warming and coral bleaching are patchy phenomena over a wide range of scales. This paper is part of a larger study that aims to understand the relationship between heat stress and ecological impact caused by the 2002-bleaching event in the Great Barrier Reef (GBR). We used a Bayesian belief network (BBN) as a framework to refine our prior beliefs and investigate dependencies among a series of proxies that attempt to characterize potential drivers and responses: the remotely sensed environmental stress (sea surface temperature — SST); the geographic setting; and topographic and ecological attributes of reef sites for which we had field data on bleaching impact. Sensitivity analyses helped us to refine and update our beliefs in a manner that improved our capacity to hindcast areas of high and low bleaching impact. Our best predictive capacity came by combining proxies for a site’s heat stress in 2002 (remotely sensed), acclimatization temperatures (remote sensed), the ease with which it could be cooled by tidal mixing (modeled), and type of coral community present at a sample of survey sites (field data). The potential for the outlined methodology to deliver a transparent decision support tool to aid in the process of identifying a series of locations whose inclusion in a network of protected areas would help to spread the risk of bleaching is discussed.

Keywords

Coral bleachingBayesian belief networksRemotely-sensed SSTGreat Barrier Reef

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  1. 1.Australian Institute of Marine ScienceTownsvilleAustralia
  2. 2.Cooperative Research Centre for the Great Barrier Reef World Heritage AreaTownsvilleAustralia