Integrating Science and Management to Assist with the Response to Stony Coral Tissue Loss Disease Active
A USGS multi-disciplinary team will use laboratory and modeling approaches to investigate the cause of stony coral tissue loss disease.
The Science Issue and Relevance: Coral reefs in Florida and the Caribbean are currently experiencing a multi-year mortality event linked to stony coral tissue loss disease (SCTLD), which has resulted in massive die-offs in multiple coral species. The cause of SCTLD remains a key knowledge gap hindering efforts to manage and mitigate the impacts of the disease. For instance, conflicting results between histological investigations showing no evidence of bacteria and field treatments showing improvement of clinical signs with antibiotic treatment makes it difficult to settle on a focused management direction. The goal of our project is to increase understanding of the cause (etiology) of SCTLD and to provide a structured approach to organizing scientific knowledge regarding the etiology of the disease to assist managers in leveraging all available information to make management decisions. To achieve our goal, a multi-disciplinary team will use laboratory and modeling approaches to investigate the etiology of this disease and create decision support tools that allow managers to take action to protect coral communities in the face of uncertainty regarding the etiological agent(s).
Methodology for Addressing the Issue: To help overcome this knowledge gap, the project will apply modeling techniques such as Bayesian belief networks (BBNs) that combine and unify all existing etiological information into a formal statistical structure (e.g., Korb and Nicholson 2004; Seixas et al. 2014). This model will produce a probabilistic assessment of the likelihood of various etiological agents based on the current knowledge and available data.
Future Steps: This project aims to provide a flexible decision support tool to help managers draw from existing knowledge on SCTLD while also creating a road map for future efforts that can maximize the efficiency of management and research efforts. Understanding the agents and drivers of SCTLD can help managers and decision-makers identify what management efforts should be prioritized, and what key research efforts are needed to reduce uncertainty associated with identifying the etiological agents.
A USGS multi-disciplinary team will use laboratory and modeling approaches to investigate the cause of stony coral tissue loss disease.
The Science Issue and Relevance: Coral reefs in Florida and the Caribbean are currently experiencing a multi-year mortality event linked to stony coral tissue loss disease (SCTLD), which has resulted in massive die-offs in multiple coral species. The cause of SCTLD remains a key knowledge gap hindering efforts to manage and mitigate the impacts of the disease. For instance, conflicting results between histological investigations showing no evidence of bacteria and field treatments showing improvement of clinical signs with antibiotic treatment makes it difficult to settle on a focused management direction. The goal of our project is to increase understanding of the cause (etiology) of SCTLD and to provide a structured approach to organizing scientific knowledge regarding the etiology of the disease to assist managers in leveraging all available information to make management decisions. To achieve our goal, a multi-disciplinary team will use laboratory and modeling approaches to investigate the etiology of this disease and create decision support tools that allow managers to take action to protect coral communities in the face of uncertainty regarding the etiological agent(s).
Methodology for Addressing the Issue: To help overcome this knowledge gap, the project will apply modeling techniques such as Bayesian belief networks (BBNs) that combine and unify all existing etiological information into a formal statistical structure (e.g., Korb and Nicholson 2004; Seixas et al. 2014). This model will produce a probabilistic assessment of the likelihood of various etiological agents based on the current knowledge and available data.
Future Steps: This project aims to provide a flexible decision support tool to help managers draw from existing knowledge on SCTLD while also creating a road map for future efforts that can maximize the efficiency of management and research efforts. Understanding the agents and drivers of SCTLD can help managers and decision-makers identify what management efforts should be prioritized, and what key research efforts are needed to reduce uncertainty associated with identifying the etiological agents.