Terrestrial watershed connections to water quality on Florida's Coral Reef

This study identified hydrographic connections between inland water sources in south Florida and coral reefs and investigated the environmental drivers associated with nutrient measurements at water quality monitoring stations between 2018 and 2024. The most significant outcomes were: 1) exposure of analytes to reefs depends on location: exposure is northward of their adjacent inlet on the east coast and more localized in southern embayments, 2) reducing nutrient loads at Government Cut would have the largest single source downstream effect, 3) WQ samples are affected by co-varying factors of sample program, sample location, depth, season, and the amounts of recent rainfall, wind, and nearby inlet outflow, 4) analyzing WQ data requires the control of these variables through modeling, 5) analyte concentrations on the east coast increased until 2022 and have plateaued coinciding with recent fertilizer restrictions and drought conditions, 6) WQ sample program was the single most influential predictor indicating monitoring program must be a factor in analyses containing multiple programs, 7) significance between land-based drivers and WQ decreased with distance from source indicating that Florida’s mainland is not the main source of nutrients throughout the Keys.
 

This study provides crucial insights into the hydrographic dynamics affecting coral reefs in South Florida. Identifying the relationship between nutrient sources and WQ sample sites lays the groundwork for effective management actions to mitigate impacts and promote reef resilience. These findings highlight the importance of managing inland water quality to protect coral reefs. The model provides a detailed understanding of how nutrients from inland sources are transported to reefs, offering valuable insights for targeted intervention strategies. Efforts are currently underway to refine the models and use the modeled nutrient data, compiled environmental data, and reef monitoring data to conduct machine learning models of the relationships between factors relating to reef health. This will provide a deeper understanding of the factors affecting reef health and the ability to target high impact mitigative actions and restoration strategies.