During the fall of 2014, several species of scleractinian coral in the Miami-Key Biscayne area showed patterns of tissue loss stemming from an unidentified coral disease. The outbreak continued to increase in frequency and severity as it spread north and south along the Florida Reef Tract. The need for a single coordinated data management plan was established to assist researchers who intend to comprehensively understand the diverse environmental factors that led to the disease event, the reasons for its persistence, and to construct a spatial epidemiological model. Our focus was to develop a data collection tool in Survey123 for ArcGIS to support the collection of pertinent biological information following Hurricane Irma. Additionally, this effort aimed to create a contact list for datasets currently used by federal, state, and university researchers; acquire relevant and ancillary datasets and metadata; evaluate for commonality and completeness; normalize and import based on geodatabase schema; and ingest new survey data as it became available.
Results/Conclusions
Research and monitoring efforts were located through online searches and participation in a coral disease workshop. There are 130 spreadsheet entries containing the name, contact information, description, and location of potentially useful datasets. Furthermore, 75 dataset owners were contacted through email to catalog data pertinence, update frequency, completeness, and to provide metadata. We consulted epidemiological researchers to determine essential fields within the database schema design and to select ancillary data sources suitable for modeling efforts. Two long-term and two contemporaneous coral disease survey datasets, including a rapid post-Irma assessment utilizing our survey tool, served as the basis of the geodatabase. Environmental covariate datasets included: water quality, sea surface temperature, height, and direction, chlorophyll a, benthic percent cover, and opportunistic disease observations. Biological datasets were normalized to contain consistent species-specific disease variables, corresponding columns from environmental datasets were cross-walked, and spreadsheets imported into an ArcMap geodatabase. Data aggregation techniques developed in this study provide a framework for the creation and implementation of additional ecological data management projects. Datasets obtained will allow researchers to statistically and spatially analyze the spread of the disease to suggest a pathogen, identify data gaps limiting interpretation, and to report trends in biodiversity and habitat condition.