Hawaiʻi Coral Disease database (HICORDIS): species-specific coral health data from across the Hawaiian archipelago

Caldwell, Jamie M.; Burns, John H. R.; Couch, Courtney S.; Ross, Megan; Runyon, Christina M.; Takabayashi, Misaki; Vargas-Ángel, Bernardo; Walsh, William J.; Walton, Maya; White, Darla; Williams, Gareth J.; Heron, Scott F.

doi:10.1016/j.dib.2016.07.025

Cited by 9 publications

(7 citation statements)

References 1 publication

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“…Other relevant aspect of this field that can be improved in the future is the incorporation of open science practices, making available annotated coral disease specific datasets—represented in our network as the ‘database’ topic—e.g., Caldwell et al., 2016a; Burns et al., 2016, or the Global Coral Disease Database (https://www.unep-wcmc.org/resources-and-data/global-coral-disease-database), especially considering that we found the topic ‘baseline’ as an important node in the network but most of this data is not freely available, and it would represent an invaluable resource for further analysis.…”

Section: Discussionmentioning

confidence: 99%

Systematic review and meta-analysis of 50 years of coral disease research visualized through the scope of network theory

Montilla

Ascanio

Verde

et al. 2019

PeerJ

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Coral disease research encompasses five decades of undeniable progress. Since the first descriptions of anomalous signs, we have come to understand multiple processes and environmental drivers that interact with coral pathologies. In order to gain a better insight into the knowledge we already have, we explored how key topics in coral disease research have been related to each other using network analysis. We reviewed 719 papers and conference proceedings published from 1965 to 2017. From each study, four elements determined our network nodes: (1) studied disease(s); (2) host genus; (3) marine ecoregion(s) associated with the study site; and (4) research objectives. Basic properties of this network confirmed that there is a set of specific topics comprising the majority of research. The top five diseases, genera, and ecoregions studied accounted for over 48% of the research effort in all cases. The community structure analysis identified 15 clusters of topics with different degrees of overlap among them. These clusters represent the typical sets of elements that appear together for a given study. Our results show that while some coral diseases have been studied considering multiple aspects, the overall trend is for most diseases to be understood under a limited range of approaches, e.g., bacterial assemblages have been considerably studied in Yellow and Black band diseases while immune response has been better examined for the aspergillosis-Gorgonia system. Thus, our challenge in the near future is to identify and resolve potential gaps in order to achieve a more comprehensive progress on coral disease research.

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Section: Discussionmentioning

confidence: 99%

Systematic review and meta-analysis of 50 years of coral disease research visualized through the scope of network theory

Montilla

Ascanio

Verde

et al. 2019

PeerJ

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“…In total, we assembled over 42,000 coral health monitoring surveys between 2012 and 2020. Data came from the NOAA NCRMP, University of Guam, Hawaii Coral Disease Database (Caldwell, Burns, et al, 2016), and the Great Barrier Reef Marine Park Authority (GBRMPA; referred to as Reef Authority in other contexts) Reef Health and Impact Surveys (Beeden et al, 2014). The different survey protocols used to collect these data have been described in detail previously (Beeden et al, 2014;Caldwell, Burns, et al, 2016;Winston et al, 2020).…”

Section: Coral Disease Survey Datamentioning

confidence: 99%

“…Data came from the NOAA NCRMP, University of Guam, Hawaii Coral Disease Database (Caldwell, Burns, et al, 2016), and the Great Barrier Reef Marine Park Authority (GBRMPA; referred to as Reef Authority in other contexts) Reef Health and Impact Surveys (Beeden et al, 2014). The different survey protocols used to collect these data have been described in detail previously (Beeden et al, 2014;Caldwell, Burns, et al, 2016;Winston et al, 2020). For the research described in this paper, there are notable methodological differences between surveys conducted in Australia and the U.S. Pacific; therefore, we modeled disease risk in these two regions separately.…”

Section: Coral Disease Survey Datamentioning

confidence: 99%

Multi-Factor Coral Disease Risk Forecasting for Early Warning and Management

Caldwell,

Liu,

Geiger

et al. 2023

Preprint

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Ecological forecasts are becoming increasingly valuable tools for conservation and management. However, there are few examples of near-operational forecasting systems that account for the wide range of ecological complexities. We developed a new experimental coral disease ecological forecasting system that explores a suite of ecological relationships and their uncertainty and investigates how forecasts operationally change with shorter lead times. The Multi-Factor Coral Disease Risk product uses a combination of ecological and marine environmental conditions to predict risk of white syndromes and growth anomalies across reefs in the central and western Pacific and east coast of Australia and is available through the U.S. National Oceanic and Atmospheric Administration Coral Reef Watch program. This product produces weekly forecasts for a moving window of six months at ~5 km resolution based on quantile regression forests. The forecasts show superior skill at predicting disease risk on withheld survey data from 2012-2020 compared with predecessor models, with the biggest improvements shown for predicting disease risk at mid- to high-disease levels. Most of the prediction uncertainty arises from model uncertainty and therefore prediction accuracy and precision do not improve substantially with shorter lead times. This result arises because many predictor variables cannot be accurately forecasted, which is a common challenge across ecosystems. Weekly forecasts and scenarios can be explored through an online decision support tool and data explorer, co-developed with end-user groups to improve use and understanding of ecological forecasts. The models provide near real-time disease risk assessments and allow users to refine predictions and assess intervention scenarios. This work advances the field of ecological forecasting with real world complexities, and in doing so, better supports near term decision making for coral reef ecosystem managers and stakeholders. Secondarily, we identify clear needs and provide recommendations to further enhance our ability to forecast coral disease risk.

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“…Methods coral health observations. We used coral health observations from the Hawaii Coral Disease Database (HICORDIS) 51 and additional surveys collected by the authors following the same methodologies used in HICORDIS (e.g., 10 × 1 m 2 belt transects surveys) resulting in 362,366 coral health observations collected between 2004 and 2015. We separated the data by host (species or genus of interest) and disease type (growth anomalies or tissue loss) and used only observations with available data for all hypothesized risk factors (predictor variables).…”

Section: Risk Factormentioning

confidence: 99%

Case-control design identifies ecological drivers of endemic coral diseases

Caldwell

Aeby

Heron

et al. 2020

Sci Rep

Self Cite

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endemic disease transmission is an important ecological process that is challenging to study because of low occurrence rates. Here, we investigate the ecological drivers of two coral diseases-growth anomalies and tissue loss-affecting five coral species. We first show that a statistical framework called the case-control study design, commonly used in epidemiology but rarely applied to ecology, provided high predictive accuracy (67-82%) and disease detection rates (60-83%) compared with a traditional statistical approach that yielded high accuracy (98-100%) but low disease detection rates (0-17%). Using this framework, we found evidence that 1) larger corals have higher disease risk; 2) shallow reefs with low herbivorous fish abundance, limited water motion, and located adjacent to watersheds with high fertilizer and pesticide runoff promote low levels of growth anomalies, a chronic coral disease; and 3) wave exposure, stream exposure, depth, and low thermal stress are associated with tissue loss disease risk during interepidemic periods. Variation in risk factors across host-disease pairs suggests that either different pathogens cause the same gross lesions in different species or that the same disease may arise in different species under different ecological conditions.Disease is an ecologically and evolutionarily important process in shaping populations, communities, and ecosystems 1-5 , but identifying factors that promote disease at low endemic levels or between epidemics is challenging because disease occurrences are rare in space and time. Disease can affect populations, communities, and ecosystems through multiple pathways, such as changing the physiology, behavior, distribution, abundance, and fitness of organisms (e.g. 3,6,7 ). For example, when the trematode Plagioporous sp. infects the coral Porites compressa it reduces coral growth and causes infected polyps to appear as bright swollen nodules that cannot retract into their skeletal cups, thus increasing fish predation and affecting coral physiology, behavior, and fitness 8 . As another example, infection in three-spined sticklebacks by the fish parasite Gyrodactylus spp. has been shown to alter the zooplankton community structure and nutrient cycling in streams and lakes, which in turn, affect the survival and fitness of the subsequent fish generation 5 . Despite the ecological and evolutionary importance of disease, understanding the factors that increase individual disease risk for endemic diseases with low prevalence or during interepidemic periods for diseases with epidemic cycles can be challenging because of the low probability of observing diseased individuals during non-outbreak periods.With limited occurrence data relative to nonoccurrence data, commonly used biostatistics such as logistic regression can significantly underestimate the probability of rare events such as disease occurrence 9 . Further, such models are likely to have high predictive accuracy (proportion of correctly identified event and nonevent observations), which can be mis...

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Hawaiʻi Coral Disease database (HICORDIS): species-specific coral health data from across the Hawaiian archipelago

Cited by 9 publications

References 1 publication

Systematic review and meta-analysis of 50 years of coral disease research visualized through the scope of network theory

Systematic review and meta-analysis of 50 years of coral disease research visualized through the scope of network theory

Multi-Factor Coral Disease Risk Forecasting for Early Warning and Management

Case-control design identifies ecological drivers of endemic coral diseases

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