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

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dc.contributor Australian Institute Of Marine Science (aims) en
dc.contributor Cooperat Res Ctr Great Barrier Reef World Heritag
dc.contributor Australian Inst Marine Sci
dc.contributor Australian Institute Of Marine Science
dc.contributor.author DONE, T
dc.contributor.author WOOLDRIDGE, S
dc.date.accessioned 2017-03-21T01:09:55Z
dc.date.accessioned 2013-02-28T06:52:44Z
dc.date.accessioned 2013-02-28T06:52:44Z
dc.date.accessioned 2019-07-08T02:23:49Z
dc.date.available 2017-03-21T01:09:55Z
dc.date.available 2013-02-28T06:52:44Z
dc.date.available 2013-02-28T06:52:44Z
dc.date.available 2019-07-08T02:23:49Z
dc.date.issued 2004-04-01
dc.identifier 6624 en
dc.identifier.citation Wooldridge SA and Done TJ (2004) Learning to predict large-scale coral bleaching from past events: A Bayesian approach using remotely sensed data, in-situ data, and environmental proxies. Coral Reefs. 23: 96-108. en
dc.identifier.issn 0722-4028
dc.identifier.uri http://epubs.aims.gov.au/11068/6624
dc.description Link to abstract/full text - http://dx.doi.org/10.1007/s00338-003-0361-y en
dc.description.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.
dc.description.uri http://dx.doi.org/10.1007/s00338-003-0361-y en
dc.language English
dc.language en en
dc.relation.ispartof Coral Reefs - pages: 23: 96-108 en
dc.relation.ispartof Null
dc.relation.uri http://data.aims.gov.au/metadataviewer/uuid/bf722d7c-4659-471d-9864-6a8960c72a70 en
dc.relation.uri http://data.aims.gov.au/metadataviewer/uuid/23647e00-c556-11dc-b99b-00008a07204e en
dc.subject Reef
dc.subject Marine & Freshwater Biology
dc.subject Remotely-sensed Sst
dc.subject Bayesian Belief Networks
dc.subject Great Barrier Reef
dc.subject Coral Bleaching
dc.title Learning to predict large-scale coral bleaching from past events: A Bayesian approach using remotely sensed data, in-situ data, and environmental proxies
dc.type journal article en
dc.identifier.doi 10.1007/s00338-003-0361-y
dc.identifier.wos WOS:000220937200010


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