Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Supplemental Files
• Related Publications
• Related Datasets
• Parameters
• Project Information
• Funding

Location: Brewer’s Bay (18.34403, -64.98435), St. Thomas, The U.S. Virgin Islands

Disease prevalence was recorded as the percentage of individuals infected by the end of the seven-day study. Disease prevalence among species was compared using a Fisher’s Exact Test in R. A photograph and timestamp was captured upon appearance of lesions and then immediately before each fragment was culled around 30% tissue loss. Disease severity was measured by calculating the rate of lesion progression across the coral fragment as the amount of tissue lost between the appearance of the lesion to the time it was culled divided by that time period. Time to infection was measured by the number of days it took for each coral fragment to show lesions throughout the seven-day study and visualized with a survival plot through a Kaplan-Meier estimate of the survivorship by using the survfit function in the R package survival (Therneau, 2020). The relative risk of each species was also calculated as: “Relative risk (RR) = Risk in exposed / Risk in non-exposed” where the risk in exposed individuals was calculated as the prevalence (diseased/total population) of those exposed to disease and risk in non-exposed individuals was calculated as the prevalence (diseased/total population) of those not exposed to disease.

Disease transmission and phenotypic sampling matched the methods from this published study “Species-specific susceptibility to white plague disease in three common Caribbean corals” Williams et al. (2020). Dr. Marilyn Brandt was the lead PI in that investigation and a CO-PI in the investigation being submitted.

Instruments: 
Band Saw to fragment colonies. Camera and ruler to take photographs. Hammer and chisel to take samples of culled coral. Samples flash frozen in liquid nitrogen, stored in -80˚C until shipped via dry shipper to The University of Texas at Arlington for molecular work. 

Lesion progression rate and percent tissue loss was calculated from photographs processed through ImageJ. Relative Risk was calculated using this equation: “Relative risk (RR) = Risk in exposed / Risk in non-exposed” which uses Markov Chain Monte Carlo simulations with Gibbs Sampling in OpenBUGS (OpenBUGS (MRC Biostatistics Unit, Cambridge, UK). Days to infections was visualized with a survival plot through a Kaplan-Meier estimate of the survivorship by using the survfit function in the R package survival (Therneau, 2020). Statistical significance comparisons and visualizations were processed in R v3.5.1 (Rstudio v1.2.5033).

BCO-DMO Data Manager Processing Notes:
* Extracted data submitted in Excel file "EAGER_PhenotypeDATA_BCODMO_Submission.xlsx" to csv
* added a conventional header with dataset name, PI name, version date
* modified parameter names to conform with BCO-DMO naming conventions: only A-Za-z0-9 and underscore allowed.  Can not start with a number.  (spaces, +, and - changed to underscores). 
* Rounded Time frame (hours) and (days) to three decimal places.
* removed percent symbol from values in percent_lost,percent_lost_per_min,percent_lost_per_hour,percent_lost_per_day so they could be typed correctly as numeric.  Units provided in parameter information.
* Provided species names checked using the World Register of Marine Species taxa match tool.  Corrected species name in the dataset Sidereastrea siderea ->   Siderastrea siderea  http://www.marinespecies.org/aphia.php?p=taxdetails&id=207516
* Species list with exact match to AphiaID taxonomic identifiers added to supplemental documents.

NSF abstract:

Coral diseases have increased significantly throughout the past 30 years. Climate change and other detrimental environment factors are likely to blame. Unhealthy coral reefs cannot support the fish and other life that make the reef a vibrant and diverse ecosystem. Corals reefs in the Caribbean Sea are disease hotspots and many reefs have experienced population collapses due to outbreaks of disease. Importantly, coral species vary in their susceptible to disease, but the reasons behind this variation are unknown. This project will quantify coral susceptibility to disease by examining coral immunity using several novel approaches and experiments. Seven species of coral that differ in disease susceptibility, growth rates, growth form and reproductive strategies will be used. Immune responses of each species of coral will be measured by exposing the corals to bacterial immune stimulators. Susceptibility to white plague disease, a prevalent disease affecting many species of corals, will also be measured by exposing the corals to active white plague disease and calculating disease transmission rates. The immune response and disease transmission data for each coral species will be used to develop a predictive model to determine how different coral communities will respond to disease threats under climate change scenarios. This project will support graduate students at University of Texas, Arlington (Hispanic-serving Institution) and University of Virgin Islands (Historically Black University) and many undergraduate students at all three institutions (Mote Marine Laboratory). This research will be highlighted at outreach events at all three institutions which take place regularly and include Earth Day Texas in Dallas, TX, Mote's Living Reef Exhibit and Aquarium in Sarasota, FL and "Reef Fest" and Agricultural fairs in the U.S. Virgin Islands.

Environmental changes, such as ocean warming, have led to an increase in the prevalence of coral diseases, causing region-wide population collapses in some locations. However, not all coral species, or even populations within species, are affected by disease equally. Some species are host to many different types of diseases, but have limited mortality. Other species suffer significant disease-related mortality. How and why disease susceptibility differs among species and the effects of this differential susceptibility on reef community structure and composition are currently unknown. This project will use immune-challenge experiments that will quantify novel components of the innate immune system of corals, coupled with the application of a trait-based model, to fulfill three goals: 1) Determine variability of coral immune traits in seven common coral species found on Caribbean reefs, 2) Determine the variability in resistance to white plague disease transmission in the same coral species 3) Develop a predictive model of coral community assemblage that incorporates immune traits. Quantification of coral immunity will also incorporate unique approaches, such as combining full transcriptome sequencing with protein activity assays for a gene-to-phenotype analysis. Data will be mapped onto immune pathways for comprehensive pathway evaluation between coral species and these will serve as trait inputs into a "traitspace" model. These traits will provide continuous data within the model, which will create a probability density function (PDF) for the trait distributions of each species. These PDFs will then be used to determine the probability of species under different disease exposure scenarios. Model analyses will determine which traits influence community structure and characterize how disease exposure and the immune response will predict community assemblages through space and time. The completion and application of a trait-base model that incorporates extensive immunity parameters (none of which have been applied to trait models within coral ecosystems) is a distinct product from this project.