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.
Highlights
Climate change and associated ocean warming have been linked to increasing frequency and severity of infectious diseases in several economically and ecologically important marine organisms [1,2]
Using the method described in Heron et al [17], we defined the occurrence of a disease outbreak
Using the method described in Heron et al [17], we defined the occurrence of a disease outbreak for for Montipora white syndrome, Porites growth anomalies and Montipora white syndrome, Porites growth anomalies and Poritestissue tissueloss losssyndrome syndromeininHawai’i
Summary
Climate change and associated ocean warming have been linked to increasing frequency and severity of infectious diseases in several economically and ecologically important marine organisms [1,2]. Bruno et al used the weekly SST anomalies metric (WSSTA; the number of weeks in the preceding year at or above +1 ̋ C above the weekly mean), coral cover and long-term Acropora white syndrome observations to model outbreak events on the Great Barrier Reef (GBR) [20] They found that sites with >50% coral cover and more than five anomalously warm weeks were associated with higher disease abundance. We evaluate the applicability of SST-based metrics describing anomalously warm and cold conditions for forecasting three common coral diseases in Hawai’i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome These diseases have caused significant morbidity, reduced fecundity, and often colony mortality in the region’s dominant reef-building corals. To determine outbreak risk in this study, we used boosted regression trees, an ensemble statistical modeling approach that allowed us to identify optimal predictors and their relative importance while minimizing predictive deviance
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