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Remote Sens. 2016, 8(2), 93; doi:10.3390/rs8020093

Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago

1
Hawai’i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai’i, Kāne‘ohe, HI 96744, USA
2
Coral Reef Watch, U.S. National Oceanic and Atmospheric Administration, College Park, MD 20740, USA
3
Marine Geophysical Laboratory, Physics Department, College of Science, Technology and Engineering, James Cook University, Townsville, QLD 4811, Australia
4
Global Science and Technology, Inc., Greenbelt, MD 20770, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Stuart Phinn, Chris Roelfsema, Xiaofeng Li, Raphael M. Kudela and Prasad S. Thenkabail
Received: 15 September 2015 / Revised: 15 December 2015 / Accepted: 20 January 2016 / Published: 26 January 2016
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
View Full-Text   |   Download PDF [1261 KB, uploaded 26 January 2016]   |  

Abstract

Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we investigated seasonal effects of thermal stress on the prevalence of the three most widespread coral diseases in Hawai’i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome. To predict outbreak likelihood we compared disease prevalence from surveys conducted between 2004 and 2015 from 18 Hawaiian Islands and atolls with biotic (e.g., coral density) and abiotic (satellite-derived sea surface temperature metrics) variables using boosted regression trees. To date, the only coral disease forecast models available were developed for Acropora white syndrome on the Great Barrier Reef (GBR). Given the complexities of disease etiology, differences in host demography and environmental conditions across reef regions, it is important to refine and adapt such models for different diseases and geographic regions of interest. Similar to the Acropora white syndrome models, anomalously warm conditions were important for predicting Montipora white syndrome, possibly due to a relationship between thermal stress and a compromised host immune system. However, coral density and winter conditions were the most important predictors of all three coral diseases in this study, enabling development of a forecasting system that can predict regions of elevated disease risk up to six months before an expected outbreak. Our research indicates satellite-derived systems for forecasting disease outbreaks can be appropriately adapted from the GBR tools and applied for a variety of diseases in a new region. These models can be used to enhance management capacity to prepare for and respond to emerging coral diseases throughout Hawai’i and can be modified for other diseases and regions around the world. View Full-Text
Keywords: disease outbreaks; corals; SST metrics; cold snaps; hot snaps; winter condition; MPSA; boosted regression trees; Hawaiian archipelago; models disease outbreaks; corals; SST metrics; cold snaps; hot snaps; winter condition; MPSA; boosted regression trees; Hawaiian archipelago; models
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Caldwell, J.M.; Heron, S.F.; Eakin, C.M.; Donahue, M.J. Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago. Remote Sens. 2016, 8, 93.

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